Improving Database Vendors’ Usage Statistics Reporting through Collaboration between Libraries and Vendors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article reports the results from the Association of Research Libraries (ARL) E-Metrics study to investigate issues associated with the usage statistics provided by database vendors. The ARL E-Metrics study was a concerted effort by twenty-four ARL libraries to develop and test statistics and measures in order to describe electronic resources and services in ARL libraries. This article describes a series of activities and investigations that included a meeting with major database vendors and the field-testing of usage statistics from eight major vendors to evaluate the degree to which the reports are useful for library decision-making. Overall, the usage statistics from the vendors studied are easy to obtain and process. However, the standardization of key usage statistics and reporting format is critical. Validation of reported statistics also remains a critical issue. This article offers a set of recommendations for libraries and calls for continuous collaboration between libraries and major database vendors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.001 | 0.002 |
| 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 it