The Development, Testing, and Evaluation of the Archival Metrics Toolkits
Bibliographic record
Abstract
This paper reports on the Archival Metrics Project, which developed, tested, and evaluated a set of toolkits designed to overcome some of the challenges of conducting user-based evaluation in college and university archival repositories. The Archival Metrics Project is ongoing. The initial toolkits result from a five-year, two-phase project funded by the Andrew W. Mellon Foundation. The project involved academics from three North American universities and twenty partners from academic archival institutions. At the completion of the study, the researchers interviewed ten archivists at partner institutions who took part in the testing of the toolkits and one year later gathered data using a questionnaire from fifty-nine individuals who downloaded the toolkits. The paper describes previous research on user-based evaluation in archives and similar projects conducted in the library field, the process of developing and testing five questionnaires and various methods to administer the questionnaires, as well as ...
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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 | 0.220 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".