University Journal Subscription Costs in the UK: An Exploratory Study
Bibliographic record
Abstract
This poster project focuses on using visual analytics to understand complex data. For this project, we investigated a unique new dataset, “Journal subscription costs - FOIs to UK universities,” published by Stuart Lawson and Ben Meghreblian. They collected data by sending Freedom of Information (FOI) requests to universities in the UK for information on the subscription costs to seven major publishers over a five year period, using the website whatdotheyknow.com. This work is ongoing, with data added as universities respond. The dataset was also chosen due to its CC-BY license, which allowed us the flexibility to remix and build upon the original work. We also supplemented this dataset with publically available UK enrolment data. Database and journal subscription costs are becoming an increasingly hot issue for academic libraries. Many libraries sign non-disclosure agreements with publishers, which limits their ability to leverage or bargain over subscription prices. Although it is often reported that subscription costs have been on the rise and that libraries are under increasing financial pressure, it is unclear exactly how 2 much libraries are paying and to whom. What is clear is that these increasing subscription prices have caused many to question the traditional publishing model, and whether the added value from traditional publishing is worth the cost. A recent Nature news feature, for example, discussed the benefits of an Open Access journals model that is able to operate profitably and more efficiently while free access to articles for the public. Other recent research by Bergstrom et al. has also used FOI requests to find information about journal subscription prices in the United States. When comparing their data with factors such as total university enrolment, number of PhD students, and whether the school was a Research 1 or Research 2 institution, they concluded: The contracts that we have seen show remarkable institution specific price variations that cannot be explained by university characteristics such as enrolment and PhD production. Some institutions have been quite successful in bargaining for lower prices, whereas others may not have been aware that better bargains can be reached. Perhaps this variation explains publishers’ desire to keep contract terms confidential (Bergstrom, Courant, McAfee, & Williams, 2014). By using a similar dataset, and merging the data with openly available UK university enrolment and geolocation data, this project builds upon research by Bergstrom et al. and, in the process, contributes to a new body of research creating transparency in the library purchasing field. This research is preliminary in nature. At the time of data cleaning and analysis, the datasets were still incomplete. However, we investigate the following questions: • How have subscription costs generally changed over the 5-year period from 2010-2014? • How does university enrolment compare with the amount that universities pay major publishers? • Does the geospatial location of a university play a role in how much they pay in subscription costs to major publishers? As the data is not very granular, it is difficult to answer any of these questions with certainty; however, we discovered several patterns that warrant further investigation. Why, for example, does it appear that institutions with average enrolment size pay the most for journal subscriptions, while those with the lowest and highest enrolment appear to pay less? Why are there so many drastic changes in price year to year at many universities? Future research should focus on acquiring more granular data and investigating these inconsistencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".