Improving Database Vendors’ Usage Statistics Reporting through Collaboration between Libraries and Vendors
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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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 | 0.369 | 0.495 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".