Collection analysis techniques used to evaluate a graduate-level toxicology collection.
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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