Measuring Cost per Use of Library-Funded Open Access Article Processing Charges: Examination and Implications of One Method
Bibliographic record
Abstract
INTRODUCTION Libraries frequently support their open access (OA) fund using money from their collections budget. Interest in assessment of OA funds is arising. Cost per use is a common method to assess library collections expenditures. OA article processing charges (APCs) are a one-time cost for global, perpetual use. Article level metrics provide data on global, cumulative article level usage. This article examines a method and discusses the limitations and implications of using article level metrics to calculate cost per use for OA APCs. METHODS Using different APC models from two publishers, PLOS and BioMed Central, this article presents a cost per use formula for each model. RESULTS The formula for each model is demonstrated with available data. The examples suggest a very low cost per use for OA APCs after only three years. DISCUSSION Several limitations exist to obtaining article level data currently, including the nature of open access and accessibility of the data. OA articles’ usage levels are high and include use from altruistic access. Cost per use comparison with traditional publishing models is possible; however, comparison between different OA expenditures with very low costs per use may not be helpful. CONCLUSION Article level metrics can provide a means to measure cost per use of OA APCs. Libraries need increased access to article level usage data. They will also need to develop new benchmarks and expectations to evaluate APC payments, given higher usage levels for OA articles and considering altruistic access.
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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 | 0.078 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".