Collection analysis techniques used to evaluate a graduate-level toxicology collection.
Bibliographic record
Abstract
Collections librarians from academic libraries are often asked, on short notice, to evaluate whether their collections are able to support changes in their institutions' curricula, such as new programs or courses or revisions to existing programs or courses. With insufficient time to perform an exhaustive critique of the collection and a need to prepare a report for faculty external to the library, a selection of reliable but brief qualitative and quantitative tests is needed. In this study, materials-centered and use-centered methods were chosen to evaluate the toxicology collection of the University of Saskatchewan (U of S) Library. Strengths and weaknesses of the techniques are reviewed, along with examples of their use in evaluating the toxicology collection. The monograph portion of the collection was evaluated using list checking, citation analysis, and classified profile methods. Cost-effectiveness and impact factor data were compiled to rank journals from the collection. Use-centered methods such as circulation and interlibrary loan data identified highly used items that should be added to the collection. Finally, although the data were insufficient to evaluate the toxicology electronic journals at the U of S, a brief discussion of three initiatives that aim to assist librarians as they evaluate the use of networked electronic resources in their collections is presented.
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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 | 0.141 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.036 | 0.036 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".