Comparing Published Scientific Journal Articles to Their Pre-Print Versions -- Extended Version

03/26/2018 ∙ by Martin Klein, et al. ∙ Los Alamos National Laboratory 0

Academic publishers claim that they add value to scholarly communications by coordinating reviews and contributing and enhancing text during publication. These contributions come at a considerable cost: U.S. academic libraries paid 1.7 billion for serial subscriptions in 2008 alone. Library budgets, in contrast, are flat and not able to keep pace with serial price inflation. We have investigated the publishers' value proposition by conducting a comparative study of pre-print papers from two distinct science, technology, and medicine (STM) corpora and their final published counterparts. This comparison had two working assumptions: 1) if the publishers' argument is valid, the text of a pre-print paper should vary measurably from its corresponding final published version, and 2) by applying standard similarity measures, we should be able to detect and quantify such differences. Our analysis revealed that the text contents of the scientific papers generally changed very little from their pre-print to final published versions. These findings contribute empirical indicators to discussions of the added value of commercial publishers and therefore should influence libraries' economic decisions regarding access to scholarly publications.

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1 Introduction

Academic publishers of all types claim that they add value to scholarly communications by coordinating reviews and contributing and enhancing text during publication. These contributions come at a considerable cost: U.S. academic libraries paid billion for serial subscriptions in alone and this number continues to rise. Library budgets, in contrast, are flat and not able to keep pace with serial price inflation. Several institutions have therefore discontinued or significantly scaled back their subscription agreements with commercial publishers such as Elsevier and Wiley-Blackwell. We have investigated the publishers’ value proposition by conducting a comparative study of pre-print papers and their final published counterparts in the areas of science, technology, and medicine (STM). We have two working assumptions:

  1. If the publishers’ argument is valid, the text of a pre-print paper should vary measurably from its corresponding final published version.

  2. By applying standard similarity measures, we should be able to detect and quantify such differences.

In this paper we present our preliminary results based on pre-print publications from arXiv.org and bioRxiv.org and their final published counterparts. After matching papers via their digital object identifier (DOI), we applied comparative analytics and evaluated the textual similarities of components of the papers such as the title, abstract, and body. Our analysis revealed that the text of the papers in our test data set changed very little from their pre-print to final published versions, although more copyediting changes were evident in the paper sets from bioRxiv.org than those from arXiv.org. In general, our results suggest that the contents of the vast majority of final published papers are largely indistinguishable from their pre-print versions. This work contributes empirical indicators to discussions of the value that academic publishers add to scholarly communication and therefore can influence libraries’ economic decisions regarding access to scholarly publications.

2 Global Trends in Scientific and Scholarly Publishing

There are several global trends that are relevant and situate the focus of this research. The first is the steady rise in both cost and scope of the global STM publishing market. According to Michael Mabe and Mark Ware in their STM Report [13], the global STM market in was billion annually, with of this from journals ( billion) and coming directly out of library budgets. Other relevant trends are the growing global research corpus [3], the steady rise in research funding [12], and the corresponding recent increase in open access publishing [1]. One longstanding yet infrequently mentioned factor is the critical contribution of faculty and researchers to the creation and establishment of journal content that is then licensed back to libraries to serve students, faculty and researchers. For example, a Elsevier study (reported in [12]) conducted for the University of California (UC) system showed that UC research publications accounted for of all research publications in the United States between and and the UC libraries purchased all of that research back from Elsevier.

2.1 The Price of Knowledge

While there are many facets to the costs of knowledge, the pricing of published scholarly literature is one primary component. Prices set by publishers are meant to maximize profit and therefore are determined not by actual costs, but by what the market will bear. According to the National Association of State Budget Officers, states in the U.S. had budgets in with lower general fund expenditures in than just prior to the global recession in [8]. Nearly half of the states therefore had not returned to pre-recession levels of revenue and spending.

2.2 Rise in Open Access Publications

Over the last several years there has been a significant increase in open access publishing and publications in STM. Some of this increase can be traced to recent U.S. federal guidelines and other funder policies that require open access publication. Examples include such policies at the National Institutes of Health, the Wellcome Trust, and the Howard Hughes Medical Center. Bo-Christer Björk et al. [2] found that in , approximately of science papers were open access. By , another study by Hammid R. Jamali and Maijid Nabavi [5] found that of journal articles were freely available online via open access.

2.3 Pre-Print versus Final Published Versions and the Role of Publishers

In this study, we compared paper pre-prints from the arXiv.org and bioRxiv.org repositories to the corresponding final published versions of the papers. The annual budget for arXiv.org as posted on the repository’s wiki is set at an average of for .111https://confluence.cornell.edu/display/arxivpub/arXiv+Public+Wiki While we do not have access to the data to precisely determine the corresponding costs for commercial publishing, the National Center for Education Statistics found in that the market for English-language STM journals was approximately billion annually. It therefore seems safe to say that the costs for commercial publishing are orders of magnitude larger than the costs for organizations such as arXiv.org and bioRxiv.org.

Michael Mabe describes the publishers’ various roles as including, but not limited to entrepreneurship, copyediting, tagging, marketing, distribution, and e-hosting [7]. The focus of the study presented here is on the publishers’ contributions to the content of the materials they publish (specifically copyediting and other enhancements to the text) and how and to what extent, if at all, the content changes from the pre-print to the final published version of a publication. This article does not consider other roles publishers play, for example, with respect to entrepreneurship, tagging, marketing, distributing, and hosting.

3 Data Gathering

Comparing pre-prints to final published versions of a significant corpus of scholarly articles from science, technology, and medicine required obtaining the contents of both versions of each article in a format that could be analyzed as full text and parsed into component sections (title, abstract, body) for more detailed comparisons. The most accessible sources of such materials proved to be arXiv.org and bioRxiv.org.

arXiv.org is an open access digital repository owned and operated by Cornell University and supported by a consortium of institutions. At the time of writing, arXiv.org hosts over million academic pre-prints, most written in fields of physics and mathematics and uploaded by their authors to the site within the past years. The scope of arXiv.org enabled us to identify and obtain a sufficiently large comparison corpus of corresponding final published versions in scholarly journals to which our institution has access via subscription.

bioRxiv.org is an open access repository devoted specifically to unrefereed pre-prints (papers that have not yet been peer-reviewed for publication) in the life sciences, operated by Cold Spring Harbor Laboratory, a private, nonprofit research institution. It began accepting papers in late and at the time of writing hosts slightly more than pre-prints. bioRxiv is thus much smaller than arXiv, and most of the corresponding final published versions in our bioRxiv data set were obtained via open access publications, rather than those accessible only via institutional subscriptions. Nonetheless, because bioRxiv focuses on a different range of scientific disciplines and thus archives pre-prints of papers published in a largely distinct set of journals, an analysis using this repository provides an informative contrast to our study of arXiv.

3.1 arXiv Corpus

Gathering pre-print texts from arXiv.org proceeded via established public interfaces for machine access to the site data, respecting their discouragement of indiscriminate automated downloads.222https://arxiv.org/help/robots

We first downloaded metadata records for all articles available from arXiv.org through February of via the site’s Open Archives Initiatives Protocol for Metadata Harvesting (OAI-PMH) interface333http://export.arxiv.org/oai2?verb=Identify. We received records in all, which provided standard Dublin Core metadata for each article, including its title and authors, as well as other useful data for subsequent analysis, such as the paper’s disciplinary category within arXiv.org and the upload dates of its versions (if the authors submitted more than one version). The metadata also contained the text of the abstract for most articles. Because the abstracts as well as the article titles often contained text formatting markup, however, we preferred to use instances of these texts that we derived from other sources, such as the PDF version of the paper, for comparison purposes (see below).

arXiv.org’s OAI-PMH metadata record for each article contains a field for a DOI, which we used as the key to match pre-print versions of articles to their final published versions. arXiv.org does not require DOIs for submitted papers, but authors may provide them voluntarily. article records in our initial metadata set () contained a DOI. Working under the assumption that the DOIs are correct and sufficient to identify the final published version of each article, we then queried the publisher-supported CrossRef citation linking service444https://github.com/CrossRef/rest-api-doc/blob/master/rest_api.md to determine whether the full text of the corresponding published article would be available for download via UCLA’s institutional journal subscriptions.

To begin accumulating full articles for text comparison, we downloaded PDFs of every pre-print article from arXiv.org with a DOI that could be matched to a full-text published version accessible through subscriptions held by the UCLA Library. Our initial query indicated that up to final published versions would be accessible in this manner. The main reason why this number is fairly low is that, at the time of writing, the above mentioned CrossRef API is still in its early stages and only a few publishers have agreed to making their articles available for text and data mining via the API. However, while this represented a very small proportion of all papers with DOI-associated pre-prints stored in arXiv.org (% at the time of the analysis), the resulting collection nevertheless was sufficient for a detailed comparative analysis. Statistically, a random sample of this size would be more than adequate to provide a % confidence level; our selection of papers was not truly random, but as noted below, the similar proportions of paper subject areas in our corpus to the proportions of subject areas among all pre-prints in arXiv.org also provides a positive indicator of its representativeness.

The downloads of pre-prints took place via arXiv.org’s bulk data access service, which facilitates the transfer of large numbers of articles as PDFs or as text markup source files and images, packaged into .tar archives, from an Amazon S3 account. Bandwidth fees are paid by the requesting party.555https://arxiv.org/help/bulk_data_s3 This approach only yields the most recent uploaded version of each pre-print article, however, so for analyses involving earlier uploaded versions of pre-print articles, we relied upon targeted downloads of earlier article versions via arXiv.org’s public web interface.

3.2 arXiv Corpus of Matched Articles

Obtaining the final published versions of article pre-prints from arXiv.org involved querying the CrossRef API to find a full-text download URL for a given DOI. Most of the downloaded files (%) arrived in one of a few standard XML markup formats; the rest were in PDF format. Due to missing or incomplete target files, of the downloads failed entirely, leaving us with published versions for comparison. The markup of the XML files contained, in addition to the full text, metadata entries from the publisher. Examination of this data revealed that the vast majority () of articles were published between and . This time range intuitively makes sense as DOIs did not find widespread adoption with commercial publishers until the early .

Figure 1: arXiv.org categories of matched articles

The disciplines of articles in arXiv.org are dominated by physics, mathematics, statistics, and computer science.666https://arxiv.org/help/stats/2016_by_area/index We found a very similar distribution of categories in our corpus of matched articles, as shown in Figure 1. An overview of the journals in which the matched articles are published is provided in the left half of Table 1. The data shows that most of the obtained published versions (%) were published in Elsevier journals.

3.3 arXiv Corpus Data Preparation

For this study, we compared the texts of the titles, abstracts, and body sections of the pre-print and final published version of each paper in our data set. Being able to generate these sections for most downloaded papers therefore was a precondition of this analysis.

All of the pre-print versions and a small minority of final published papers () were downloaded in PDF format. To identify and extract the sections of these papers, we used the GROBID777https://github.com/kermitt2/grobid

library, which employs trained conditional random field machine learning algorithms to segment structured scholarly texts, including article PDFs, into XML-encoded text.

The markup tags of the final published papers downloaded in XML format usually identified quite plainly their primary sections. A sizable proportion () of such papers, however, did not contain a demarcated body section in the XML and instead only provided the full text of the papers. Although it is possible to segment these texts further via automatic scholarly information extraction tools such as ParsCit,888http://aye.comp.nus.edu.sg/parsCit/ which use trained conditional random field models to detect sections probabilistically, for the present study we elected simply to omit the body sections of this subset of papers from the comparison analysis.

Figure 2: bioRxiv.org subjects of matched articles

As noted above, the GROBID software used to segment the PDF papers was probabilistic in its approach, and although it was generally quite effective, it was not able to isolate all sections (title, abstract, body) for approximately % of the papers in our data set. This situation, combined with the aforementioned irregularities in the XML of a similar proportion of final published papers, meant that the number of corresponding texts for comparison varied by section. Thus, for our primary comparison of the latest pre-print version uploaded to arXiv.org to its final published version, we were able to compare directly titles and abstract sections and body sections.

The large variations in formatting of the references sections (also called the “tail”) as extracted from the raw downloaded XML and the parsed PDFs, however, precluded a systematic comparison of that section. We leave such an analysis for future work. A further consequence of our text-only analysis was that the contents of images were ignored entirely, although figure captions and the text contents of tables usually could be compared effectively.

arXiv Corpus bioRxiv Corpus
Freq Journal Freq Journal
7143 Physics Letters B 154 PLOS ONE
261 Journal of Algebra 98 Scientific Reports
229 Nuclear Physics B 91 Genetics
218 Advances in Mathematics 86 eLife
179 Biophysical Journal 69 PLOS Genetics
Nuclear Instruments and Methods in Physics Research Section A:
179 Accelerators, Spectrometers, Detectors and Associated Equipment 69 PLOS Computational Biology
175 Physics Letters A 66 PNAS
162 Journal of Mathematical Analysis and Applications 59 G3: Genes|Genomes|Genetics
154 Physica A: Statistical Mechanics and its Applications 52 Genome Biology
146 Journal of Functional Analysis 46 Nature Communications
125 Annals of Physics 44 BMC Genomics
122 Linear Algebra and its Applications 42 Genome Research
122 Nuclear Physics A 33 BMC Bioinformatics
107 Computer Physics Communications 26 Molecular Ecology
104 Journal of Pure and Applied Algebra 26 Nature Genetics
96 Topology and its Applications 25 NeuroImage
96 Journal of Number Theory 24 PeerJ
80 Theoretical Computer Science 23 Evolution
77 Stochastic Processes and their Applications 19 Nature Methods
73 Icarus 19 American Journal of Human Genetics
Table 1: Overview of top 20 journals of final published versions per corpus

3.4 bioRxiv Corpus

Compared to the arXiv papers, we were able to accumulate a smaller but overall more proportionately representative corpus of life science pre-prints and final published papers from bioRxiv.org. The repository does not as yet offer the same sophisticated bulk metadata access and PDF downloading features as arXiv.org, but fortunately the comparatively small scale of bioRxiv enabled us to collect article metadata and texts utilizing basic scripting tools. We first gathered metadata via the bioRxiv.org site’s search and browse features for all articles posted to the site from its inception in November 2013 until November 2016. For these articles, which numbered in total, we extracted the author-supplied DOIs and journal information about their eventual publication venues, when provided, as well as titles, abstracts, download links, and submission dates for all versions of the pre-prints.

3.5 bioRxiv Corpus of Matched Articles

of the pre-print records in bioRxiv contained final publication DOIs. We attempted to obtain the full texts of the published versions by querying these DOIs via the CrossRef API as described above for the arXiv papers. Relatively few of these papers — in all — were actually available in full text via this method. We then used the R ’fulltext’ package from the rOpenSci project,999https://github.com/ropensci/fulltext which also searches sources including PLOS, Biomed Central, and PMC/Pubmed, and ultimately had more success, obtaining a total of published papers with full texts and an additional publication records containing titles and abstracts but no body texts or end matter sections. Most of the primary subjects of these matched articles are in the field of biology. The corresponding overview of subject areas is provided in Figure 2. The journals in which the articles are published are provided in the right half of Table 1.

3.6 bioRxiv Corpus Data Preparation

Extraction of the data from the bioRxiv pre-print and published articles for the text comparison proceeded via a similar process to that of the arXiv data preparation: the earliest and latest versions of the matched pre-print articles (as well as a handful of final published papers only available as PDF) were downloaded as PDFs and parsed into their component sections via the GROBID software. The downloaded records of the final published versions were already separated into these sections via XML markup, so rudimentary parsing routines were sufficient to extract the texts from these files. We also extracted publication dates from these records to facilitate the timeline analyses shown below.

4 Analytical Methods

We applied several text comparison algorithms to the corresponding sections of the pre-print and final published versions of papers in our test data set. These algorithms, described in detail below, were selected to quantify different notions of “similarity” between texts. We normalized the output values of each algorithm to lie between and , with indicating that the texts were effectively identical, and indicating complete dissimilarity. Different algorithms necessarily measured any apparent degree of dissimilarity in different ways, so the outputs of the algorithms cannot be compared directly, but it is nonetheless valid to interpret the aggregation of these results as a general indication of the overall degree of similarity between two texts along several different axes of comparison.

4.1 Editorial Changes

The well-known Levenshtein edit distance metric [6] calculates the number of character insertions, deletions, and substitutions necessary to convert one text into another. It thus provides a useful quantification of the amount of editorial intervention — performed either by the authors or the journal editors — that occurs between the pre-print and final published version of a paper. Our work used the edit ratio calculation as provided in the Levenshtein Python C Implementation Module,101010https://pypi.python.org/pypi/python-Levenshtein/0.11.2 which subtracts the edit distance between the two documents from their combined length in characters and divides this amount by their aggregate length, thereby producing a value between (completely similar) and (completely dissimilar).

4.2 Length Similarity

The degree to which the final published version of a paper is shorter or longer than the pre-print constitutes a much less involved but nonetheless revealing comparison metric. To calculate this value, we divided the absolute difference in length between both papers by the length of the longer paper and subtracted this value from . Therefore, two papers of the same length will receive a similarity score of ; this similarity score is if one paper is twice as long as the other, and so on. It is also possible to incorporate the polarity of this change by adding the length ratio to if the final version is longer, and subtracting it from if the pre-print is longer.

Figure 3: arXiv corpus - comparison results for titles

4.3 String Similarity

Two other fairly straightforward, low-level metrics of string similarity that we applied to the paper comparisons were the Jaccard and Sørensen indices, which consider only the sets of unique characters that appear in each text. The Sørensen similarity [11] was calculated by doubling the number of unique characters shared between both texts (the intersection) and dividing this by the combined sizes of both texts’ unique character sets.

The Jaccard similarity calculation [4] is the size of the intersection (see above) divided by the total number of unique characters appearing in either the pre-print or final published version (the union).

Implementations of both algorithms were provided by the standard Python string distance package.111111https://pypi.python.org/pypi/Distance/

4.4 Semantic Similarity

Comparing overall lengths, shared character sets, and even edit distances between texts does not necessarily indicate the degree to which the meaning of the texts — that is, their semantic content — actually has changed from one version to another. To estimate this admittedly more subjective notion of similarity, we calculated the pairwise cosine similarity between the pre-print and final published texts. Cosine similarity can be described intuitively as a measurement of how often significant words occur in similar quantities in both texts, normalized by the lengths of both documents

[9]. The actual procedure used for this study involved removing common English “stopwords” from each document, then applying the Porter stemming algorithm [10] to remove suffixes and thereby merge closely related words, before finally applying the pairwise cosine similarity algorithm implemented in the Python scikit-learn machine learning package121212http://scikit-learn.org/stable/ to the resulting term frequency lists. Because this implementation calculates only the similarity between two documents considered in isolation, instead of within the context of a larger corpus, it uses raw term counts, rather than term-frequency/inverse document frequency (TF-IDF) weights.

5 arXiv Corpus
Experiment Results

We calculated the similarity metrics described above for each pair of corresponding pre-print and final published papers in our data set from arXiv.org, comparing titles, abstracts, and body sections. See Section 7 for the results of running the same comparisons on the papers from bioRxiv.org. From the results of these calculations, we generated visualizations of the similarity distributions for each metric. Subsequent examinations and analyses of these distributions provided novel insights into the question of how and to what degree the text contents of scientific papers may change from their pre-print instantiations to the final published version. Because each section of a publication differs in its purpose and characteristics (e.g., length, standard formatting) and each metric addresses the notion of similarity from a different perspective, we present the results of our comparisons per section (title, abstract, and body), subdivided by comparison metric.

Figure 4: arXiv corpus - comparison results for abstracts

5.1 Title Analysis

First, we analyzed the papers’ titles. A title is usually much shorter (fewer characters) than a paper’s abstract and its body. That means that even small changes to the title would have a large impact on the similarity scores based on length ratio and Levenshtein distance. Titles also often contain salient keywords describing the overall topic of the paper. If those keywords were changed, removed or new ones added, the cosine similarity value would drop.

Figure 3 shows the comparison of results of all five text similarity measures applied to titles. Since all measures are normalized, their values range between and . Values close to indicate a very high level of similarity and values close to represent a high degree of dissimilarity of the analyzed text. Figure 3 shows results aggregated into ten bins. Each bin represents a range of the normalized score. For example, the leftmost bin represents scores between and , the bin second to the left represents scores between and , and the rightmost bin represents scores between and . The horizontal x-axis shows the ranges the bins represent. Each bin contains five bars, one for each similarity measure applied. The height of a bar indicates the number of articles whose title similarity score falls into the corresponding bin. For example, imagine an article that has the following title similarity scores: , , , , and . This article would therefore contribute to the green and dark blue bars in the leftmost bin, to the yellow bar in the bin second from the left, and to the purple and gray bars in the bin third from the left. The total height of a bar (the total number of articles) can be read from the left y-axis in absolute numbers. In addition, each bar has a red diamond that shows its magnitude relative to the size of the entire corpus. This percentage can be read from the right y-axis.

Figure 3 shows a dominance of the top bin. The vast majority of titles have a very high score in all applied similarity measures. Most noticeably, almost titles (around of all titles) are of very similar length, with a ratio value between and . The remaining fall into the next bin with values between and . A very similar observation can be made for the Levenshtein distance and the Sørensen value. About of those values fall into the top bin and the majority of the remaining values (around ) land between and . The cosine similarity is also dominated by values in the top bin (around ) but the remaining values are more distributed across the second, third, fourth, and fifth bin. Just about half of all Jaccard values can be seen in the top bin and most of the remainder is split between the second () and the third bin (). In many cases, this metric is registering low-level but systematic differences in character use between the pre-print and final published versions as filtered through the download methods described above: for example, a pre-print may consistently use em-dashes (–), whereas the published version uses only hyphens (-). This sensitivity of the Jaccard similarity score to subtle changes in the unique character sets in each text is apparent for other sections as well.

The results of this comparison, in particular the fact that the majority of values fall between and , provide very strong indicators that titles of scholarly articles do not change noticeably between the pre-print and the final published version. Even though Figure 3 shows a small percentage of titles exhibiting a rather low level of similarity, with Levenshtein and Sørensen values between and , the overall similarity of titles is very high.

Figure 5: arXiv corpus - comparison results for body sections

5.2 Abstract Analysis

The next section we compared was the papers’ abstracts. An abstract can be seen as a very short version of the paper. It often gives a brief summary of the problem statement, the methods applied, and the achievements of the paper. As such, an abstract usually is longer than the paper’s title (in number of characters) and provides more context. Intuitively, it seems probable that we would find more editorial changes in longer sections of the pre-print version of an article compared to its final published version. However, a potentially increased number of editorial changes alone does not necessarily prove dissimilarity between longer texts. We expect similarity measures based on semantic features such as cosine similarity to be more reliable here.

Figure 4 shows the comparative results for all abstracts. The formatting of the graph is the same as previously described for Figure 3. To our surprise, the figure is dominated by the high frequency of values between and across all similarity measures. More than abstracts (about ) have such scores for their length ratio, Levenshtein distance, and Sørensen index. of the remaining length ratio and Levenshtein distance values as well as of the remaining Sørensen index values fall between and

. The remaining pairs are distributed across all other bins. The cosine similarity and Jaccard index values are slightly more distributed. About

abstracts () fall into the top bin, and into the second, and and into the third bin, respectively.

Not unlike our observations for titles, the algorithms applied to abstracts predominantly return values that indicate a very high degree of similarity. Figure 4 shows that more than of abstracts score or higher, regardless of the text similarity measure applied. It is also worth pointing out that there is no noticeable increased frequency of values between and as previously seen when comparing titles (Figure 3).

5.3 Body Analysis

The next section we extracted from our corpora of scholarly articles and subjected to the text similarity measures is the body of the text. This excludes the title, the author(s), the abstract, and the reference section. This section is, in terms of number of characters, the longest of our three analyzed sections. We therefore consider scores resulting from algorithms based on editorial changes to be less informative for this comparison. In particular, a finding such as “The body of article contains fewer characters than the body of article ” would not provide any reliable indicators of the similarity between the two articles and . Algorithms based on semantic features, such as cosine similarity, on the other hand, provide stronger indicators of the similarity of the compared long texts. More specifically, cosine values are expected to be rather low for very dissimilar article body sections.

The results of this third comparison can be seen in Figure 5. The height of the bar representing the cosine similarity is remarkable. Almost body sections of our compared scholarly articles, which is equivalent to of the entire corpus, have a cosine score that falls in the top bin with values between and . have a cosine value that falls into the second and fall into the third bin. Values of the Sørensen index show a very similar pattern with in the top bin and in the second. In contrast, only of articles’ bodies have Jaccard index values falling into the top bin. The vast majority of these scores, , are between and and another are between and . It is surprising to see that even the algorithms based on editorial changes provide scores mostly in the top bins. Of the length ratio scores, fall in the top bin and in the second bin. The Levenshtein distance shows the opposite proportions: are in the top and belong to the second bin.

The dominance of bars on the left hand side of Figure 5 provides yet more evidence that pre-print articles of our corpus and their final published version do not exhibit many features that could distinguish them from each other, neither on the editorial nor on the semantic level. of all analyzed body sections have a similarity score of or higher in any of the applied similarity measures.

5.4 Publication Dates

The above results provide strong indicators that there is hardly any noticeable difference between the pre-print version of a paper and its final published version. However, the results do not show which version came first. In other words, consider the two possible scenarios:

  1. Papers, after having gone through a rigorous peer review process, are published by a commercial publisher first and then, as a later step, uploaded to arXiv.org. In this case the results of our text comparisons described above would not be surprising, as the pre-print versions would merely be a mirror of the final published ones. There would be no apparent reason to deny publishers all credit for peer review, copyediting, and the resulting publication quality of the articles.

  2. Papers are uploaded to arXiv.org first and later published by a commercial publisher. If this scenario is dominant, our comparison results would suggest that any changes in the text due to publisher-initiated copyediting are hardly noticeable.

Figure 6 shows the order of appearance in arXiv.org versus commercial venues for all articles in our corpus, comparing the publication date of each article’s final published version to the date of its latest upload to arXiv.org. Red bars indicate the amount of articles (absolute values on the y-axis) that were first upload to arXiv.org, and blue bars stand for articles published by a commercial publisher before they appeared in arXiv.org. Each pair of bars is binned into a time range, shown on the x-axis, that indicates how many days passed between the article’s appearance in the indicated first venue and its appearance in the second venue. Figure 6 shows clear evidence that the vast majority of our articles () were published in arXiv.org first. Therefore our argument for the second scenario from above holds. We can only speculate about the causes of certain time windows’ prominence within the distribution, but it may be related to turn-around times of publishers between submission and eventual publication.

Figure 6: Numbers of articles from the arXiv corpus first appearing in the specified venue, given the date of the last pre-print upload and the commercial publication date, binned by the number of days between them

6 Versions of Articles from the arXiv.org Corpus

About of all million papers in arXiv.org at time of writing have more than one version. A new version is created when, for example, an author makes a change to the article and re-submits it to arXiv.org. The evidence of Figure 6 shows that the majority of the latest versions in arXiv.org were uploaded prior to the publication of its final published version in a commercial venue. However, we were motivated to eliminate all doubt and hence decided to repeat our comparisons of the text contents of paper titles, abstracts, and body sections using the earliest versions of the articles from arXiv.org only. The underlying assumption is that those versions were uploaded to arXiv.org even earlier (if the authors uploaded more than one version) and hence are even less likely to exhibit changes due to copyediting by a commercial publisher. It follows, then, that if the comparisons of these earlier pre-print texts to their published versions show substantially greater divergences, then it is possible that more of these changes are the result of publisher-initiated copyediting.

Figure 7: Deltas resulting from the comparison of the title sections of the first uploaded pre-print to the final published paper, contrasted with the comparison of the last uploaded pre-print to the final published paper.
Figure 8: Deltas resulting from the comparison of abstracts, as in Figure 7.
Figure 9: Deltas resulting from the comparison of the body, as in Figure 7.

Our corpus of pre-print and final published papers matched by their DOIs and available via UCLA’s journal subscriptions exhibits a higher ratio of papers with more than one version in arXiv.org than is found in the full set of articles available from arXiv.org. of the papers we compared had more than one version, had exactly two, and had exactly three versions; whereas only of all articles uploaded to arXiv.org have more than one version. We applied our five similarity measures (see Section 4) to quantify the similarity between the first versions of all articles and their final published versions. Rather than repeating the histograms of Figures 3, 4, and 5, we show the divergences from these histograms only.

Figure 7 depicts the deltas of the relative values of the title comparison. The colored dots represent the similarity measures applied, and as seen in previous figures, the five colors corresponding to the five similarity measures are grouped into bins. The values on the y-axis represent the delta between the relative numbers from the last and the first versions. For example, in Figure 3, which shows the title comparison numbers of the last pre-print versions, we see that of cosine values fall into the top bin. For the title comparison of first pre-print versions, only of cosine values fall into the top bin. We subtract the former value from the latter and arrive at a value of . This value is represented by the yellow dot (for the cosine similarity measure) in the leftmost bin of Figure 7. Another example is the cosine value for the second bin. For the last versions, of values fall into this bin, compared to for the first versions. Hence the yellow dot in Figure 7 represents a value of . Therefore, if a delta value is negative, fewer comparisons of the first pre-print to the final published version result in scores that fall into the corresponding bin for the last pre-print comparsion. Positive values, on the other hand, show that more comparison scores fall into a particular bin for the first rather than the last versions. Figure 7 shows small negative values for the top bin, positive numbers for the second and third bins, and basically unchanged values for all other bins. This means the title comparison results are very similar between the last pre-print versions and the final published versions and the first pre-print versions and the final published versions. Figure 8 presents the delta values for the abstract comparisons. The numbers are fairly similar and show that the similarity of abstracts for first versions is slightly lower compared to last pre-print versions. The deltas in the second, third, fourth, and all following bins, however, are all positive, which indicate that the differences are not substantial. The numbers for the body comparison are shown in Figure 9. We can observe that the length ratio and the Levenshtein scores for the top bin are and points lower. However, the cosine scores are fairly similar, indicating that the semantic-level similarity is still high.

These results confirm our initial assessment that very little difference can be found between pre-print articles and their final published versions. Even more so, these findings strengthen our argument as they show that the difference between the earliest possible pre-print version and the final published one seems insignificant, given the similarity measures we applied to our corpus.

Figure 10: Numbers of articles in the arXiv corpus first appearing in the specified venue, given the date of the first pre-print upload and the commercial publication date, binned by the number of days between them
Figure 11: bioRxiv corpus - comparison results for titles

6.1 Publication Dates of Versions

The scenarios discussed in Section 5.4 with respect to the question of whether an article was uploaded to arXiv before it appeared in a commercial venue are valid for this comparison as well. Figure 10 mirrors the concept of Figure 6 and shows the number of earliest pre-print versions uploaded to arXiv.org first in red and the final published versions appearing first represented by the blue bars. As expected, the amount of pre-print versions published first increased and now stands at as shown in Figure 10 (compared to shown in Figure 6). Our argument for the second scenario described above is therefore strongly supported when considering the earliest uploaded versions of pre-prints.

7 bioRxiv Corpus
Experiment Results

We were curious whether the generally neglibible differences detected between pre-print and final published versions of articles from arXiv.org also would be prevalent among papers from a different scientific domain — thereby suggesting whether or not further replicative studies might find this phenomenon to be general across STM fields. As discussed above, the life sciences pre-print repository bioRxiv.org, which was explicitly modeled on arXiv.org, proved to be the most readily available source of pre-prints for this follow-on work, and we were able to accumulate a sufficient corpus of matching published versions for comparison. We therefore calculated similarity values for each pair of corresponding papers, again comparing titles, abstracts, and body sections, producing visualizations very similar in nature to the previously seen figures. The results of these comparisons, as presented in further detail below, shared the same overall characteristics of those from the arXiv data set, although they exhibited some differences as well. We attribute these differences primarily to divergent disciplinary practices between physics and biology (and their related fields) with respect to the degrees of formatting applied to pre-print and published articles.

Figure 12: bioRxiv corpus - comparison results for abstracts

7.1 Title Analysis

Figure 11 shows the scores of all five similarity measures for the title comparison, which is quite similar in concept to Figure 3 for the arXiv corpus. We can again observe the dominance of the top bin, with more than of length scores, of Levenshtein scores, and of Sørensen scores falling into this bin. Just less than half of all cosine and Jaccard scores also range between and . The vast majority of remaining scores fall into the second and third bin, and only cosine and Jaccard see around of scores in the fourth bin from the left. These results from the bioRxiv corpus confirm our earlier findings from the arXiv corpus that titles of scholarly articles rarely change noticeably between the pre-print and the final published version.

7.2 Abstract Analysis

The bars shown in Figure 12 represent the comparison scores for abstracts from the bioRxiv corpus. The graph is similarly dominated by the top bin, but compared to abstracts from the arXiv corpus (Figure 4) the numbers are more evenly distributed. Around half of all length, Levenshtein, and cosine values fall into the top bin, along with of Jaccard scores. Given that the majority of the remaining scores fall into the second, third, and fourth bins, we can confidently say that all similarity measures score very high for bioRxiv abstracts. We do note, however, a small percentage of scores falling into the last bin with values between and , indicating that some astracts have significantly changed in length and even in terms of semantic resemblance, which is indicated by the cosine score.

7.3 Body Analysis

Figure 13: bioRxiv corpus - comparison results for body sections

The results of the comparison between body sections are shown in Figure 13. It is astonishing to see the very high percentage of cosine scores, , in the top bin. Furthermore, of all cosine scores fall into the top three bins. The top two bins hold almost all of the Sørensen scores and the top three hold almost all Jaccard scores — both are set-based similarity scores, yet again they indicate a very high level of similarity of the compared body sections. The Levenshtein scores are, especially compared to the arXiv corpus (Figure 5), slightly shifted to the right. While the vast majority of values are above , almost no scores fall into the top bin. This observation can likely be attributed to the nature of the bioRxiv corpus, where we have observed pre-print versions adhering to simple templates that were changed for the final published version. The Levenshtein similarity measure is most sensitive to these sorts of textual differences. For the body comparison, we again see a small number of cosine scores () falling into the last bin, which indicates dramatically different content between the compared bodies of text. Regardless, the overall very high scores, especially the semantic and set-based scores, provide additional evidence that bioRxiv pre-prints and their final published versions do not exhibit many differentiating features, neither on the editorial nor on the semantic level.

Figure 14: Numbers of articles in the bioRxiv corpus first appearing in the specified venue, given the date of the last pre-print upload and the commercial publication date, binned by the number of days between them

7.4 Publication Dates

We also analyzed the scenarios discussed in Section 5.4 with respect to the question of whether or not an article appears in the pre-print repository before it is published in a journal. The ratio of bioRxiv articles appearing first as pre-prints (red bars) versus appearing first as final published versions (blue bars) is shown in Figure 14. Similar to Figure 6, the bars are binned into time ranges showing the number of days since first publication in the respective venue. Figure 14 shows that of bioRxiv articles were published as pre-print first. This number is similar to articles from the arXiv corpus, and is also not surprising given bioRxiv’s stated purpose of archiving non-refereed pre-prints. Perhaps as a consequence of this preference for pre-submission versions, we do not see the same dominance of the days time slot that was observed with arXiv articles. In fact, of bioRxiv articles that appear as pre-prints first are posted on bioRxiv anywhere from to days before their final published counterpart is published by a commercial publisher. of articles had identical dates of appearance in bioRxiv and their final published venue.

8 Versions of Articles from the bioRxiv Corpus

bioRxiv articles can have multiple versions, which is another resemblance to the arXiv repository that served as the model for its creators. A new version is generated when an author resubmits a modified verion of a paper or edits the article-specific metadata. () of the papers in our pre-print corpus from bioRxiv had more than one version (compared to in the arXiv corpus). Prompted by motivations that were quite similar to those described in Section 6, we conducted the same similarity experiments we applied to papers from the arXiv corpus with multiple pre-print versions (see Section 4) but considered only the first versions of papers as uploaded to bioRxiv and compared them to their final published counterparts. Naturally, these versions were uploaded prior to the last versions in the bioRxiv corpus that we considered in the previous experiments (detailed in Section 7) and hence, intuitively, should show fewer indicators of copyediting by commercial editors.

The visualizations of the differences between these comparisons are very similar to the previously seen Figures 7, 8, and 9 for the arXiv corpus. Figure 15 shows the differences in relative scores for the title comparisons. We can observe that for all similarity measures, fewer scores fall into the top bin. However, the majority of delta values in the following bins are positive, which means the similarity scores shift from the top bin to the second, third, and fourth bins. An expection is the delta for the cosine score, which is negative for the first four bins and just then turns positive. This indicates that there indeed are more semantic changes in the titles of the first bioRxiv versions compared to their final published versions. We do not see this pattern for the first arXiv versions (Figure 7). Figure 16 displays the changes in relative scores for the abstract comparison. We see a similar pattern with fewers scores of all similarity measures falling into the top bin. However, unlike the score changes for titles, we see here that all similarity scores have positive deltas in the following bins. In particular, a fraction of length, cosine, Sørensen, and Jaccard values that fell into the top bin for the last versions are now distributed over the second, third, and fourth bins. The Levenshtein values drop the most from the top bin and seem to frequently fall into bins representing similarity scores between and as well as and . The changes of relative values for the body comparison are shown in Figure 17. The patterns are very similar to what we have observed for titles and abstracts. The delta for the Levenshtein scores stands out, however. Figure 13 shows almost no Levenshtein scores in the top bin and Figure 17 confirms that there is almost no change to this bin. The delta of scores in the three consecutive bins is negative before turning positive in the fifth, sixth, and seventh bin. This means that the pattern shown in Figure 13 for the Levenshtein score is amplified and the scores are increasingly falling into bins that represent lower scores. It is plausible that the explanation cited above involving the authors’ use of rudimentary article templates also applies here.

Figure 15: bioRxiv corpus - deltas resulting from the comparison of the title sections of the first uploaded pre-print to the final published paper, contrasted with the comparison of the last uploaded pre-print to the final published paper.
Figure 16: bioRxiv corpus - deltas resulting from the comparison of abstracts, as in Figure 15.
Figure 17: bioRxiv corpus - deltas resulting from the comparison of the papers’ body sections, as in Figure 15.

8.1 Publication Dates of Versions

As described for the arXiv corpus in Section 6, we also investigated the publication dates of the first versions of papers uploaded to bioRxiv. Figure 18 shows the ratios of first versions of papers first appering in bioRxiv to those published by a commercial publisher first. Not unlike the ratios for last versions in bioRxiv as shown in Figure 14, we can observe a clear dominance of papers appearing in bioRxiv first. of papers fall into this category (compared to for last versions of bioRxiv papers). As these papers are first versions in bioRxiv, it is not surprising that the time difference between their pre-print upload date and the date they were finally published by a commercial publisher increases relative to the last version uploaded to bioRxiv. We made the same observation for the arXiv corpus in Figure 10, compared to Figure 6.

Figure 18: Numbers of articles in the bioRxiv corpus first appearing in the specified venue, given the date of the first pre-print upload and the commercial publication date, binned by the number of days between them.

9 Discussion and Future Work

The results outlined in this paper are from a preliminary study on the similarity of pre-print articles to their final published counterparts. There are many areas where this study could be improved and enhanced. Expanding this line of experiments to other domains such as the humanities, social sciences, and economics might return different results, as the review and editorial practices in other disciplines can vary considerably.

The matching of a pre-print version of an article to its final published version was done by means of the article’s DOI. While this is an obvious choice for a paper identifier, by only relying on DOIs we very likely missed out on other matching articles. Note also that we could only match articles that we have access to via UCLA Library’s serial subscriptions or via open access publications. It might be worth expanding the matching process to a collaborating organization with ideally complementary subscriptions to maximize access to full text articles.

One typical article section we have not analyzed as part of this research is the references section. Given publishers’ claims of adding value to this section of a scholarly article, we are motivated to see whether we can detect any significant changes between pre-prints and final published versions there. Similarly, we have not thoroughly investigated changes in the author sections. We anticipate author movement, such as authors being added, being removed, and having their rank in the list of authors changed — although changes in author order due to publishers’ name alphabetization policies must be considered as well. Initial experiments in this domain have proven difficult to interpret, as author names are provided in varying formats and normalization is not trivial.

Another angle of future work is to investigate the correlation between pre-prints and final published versions’ degree of similarity and measured usage statistics such as download numbers and the articles’ impact factor values. When arguing that the differences between pre-print articles and their final published versions are insignificant, factoring in usage statistics and “authority values” can further inform decisions about investments in serial subscriptions.

10 Conclusions

This study is motivated by academic publishers’ claims of the value they add to scholarly articles by copyediting and making further enhancements to the text. We present results from our preliminary study to investigate the textual similarity of scholarly pre-prints and their final published counterparts. We generate two different corpora from the popular pre-print services arXiv and bioRxiv and match their papers to the corresponding versions as published by commercial publishers. We use standard text extraction methods to compare individual sections of papers such as the title, abstract, and the body. For the text comparison, we apply five different similarity measures and analyze their results.

We have shown that, within the boundaries of the arXiv corpus, there are no significant differences in aggregate between pre-prints and their corresponding final published versions. The picture for the bioRxiv corpus is very similar, but we do see a slightly larger divergence between pre-print and final published paper versions in this case, suggesting that varying disciplinary practices regarding formatting and copyediting can and do influence the degree of detected similarity between pre-print and final published articles. In addition, we have shown for both corpora that the vast majority of pre-prints ( and , respectively) are published by the open access pre-print services first and later by a commercial publisher. If we consider the first ever uploaded pre-print versions, these numbers increase to and , respectively.

Given the fact of flat or even shrinking library, college, and university budgets, our findings provide empirical indicators that should inform discussions about commercial publishers’ value propositions in scholarly communication and have the potential to influence higher education and academic and research libraries’ economic decisions regarding access to scholarly publications.

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