Assessing the Value of Transformative Agreements: An Automated Approach to Estimating Article Processing Charge (APC) Discounts
Bibliographic record
Abstract
Transformative Agreements (TAs) aim to transition publishers from traditional subscription models to read-publish models that promote open access publishing through discounted or waived Article Processing Charges (APCs). As academic libraries increasingly adopt TAs with publishers, evaluating the financial impact of these agreements on libraries (and the researchers they support) remains a complex task. This presentation will demonstrate an approach to automating the estimation of APC discounts associated with TAs at a large research-intensive university. Utilizing the free OpenAlex API, the presenters developed a programmatic approach to identifying discount-eligible publications, and the APC list prices for the journals in which they appeared. By co-ordinating this data with the specific discounts associated with the university’s TAs, we were able to estimate the total potential savings these agreements provided to the university’s research community. By leveraging computational notebooks for code development (Jupyter), we automated the process of data extraction and analysis and created a clearly documented approach for assessing the value of the university’s TAs moving forward. Attendees will learn how we used the OpenAlex API to retrieve APC data and how we applied discount structures for the university’s multiple TAs. We will discuss challenges encountered, including data inconsistency, and the methods used to address these issues. Ultimately, this automated approach will allow for ongoing assessment of the financial value of TAs, aiding in future decision-making for both the library and the university. Attendees will gain insight into how computational notebooks support automated data retrieval/analysis and will be left with a template for evaluating the TAs at their own institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".