Cost per Use as an Electronic Resources Evaluation Parameter: Can You Use It Under Extraordinary Circumstances?
Bibliographic record
Abstract
In 2017, the University of Puerto Rico (UPR) suffered two extraordinary events that substantially affected library services. From March through June 2017 the university was closed due to a student strike that affected daily activities and academic services. In September of the same year, our country was hit by the most powerful hurricane ever recorded in its history, which left the whole island without power and communications infrastructure for many months. In both scenarios, access to electronic resources was seriously affected. Usage reports are important for, among other things, evidencing the use of electronic resources in a certain collection, justifying the allocation of funds, and as criteria for evaluating resources. Cost per use is one of the evaluation parameters used by many academic institutions, including the Library System at the UPR Rio Piedras Campus (UPRRP). However, what happens when there are extraordinary factors that affect the calculation of the cost per use during a period of time? What alternatives exist, if any, to be able to calculate and continue using cost per use as a reliable evaluation parameter? This work in progress proposes the development of a new way of calculating and analyzing the cost per use of the electronic subscriptions of the UPRRP Library System using data that is not influenced by extraordinary events and that may affect the final result. The use of the median instead of the average to calculate the cost per use can be an effective alternative to deal with this problem.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".