Bibliographic record
Abstract
Purposes: In this article, we describe two methods to evaluate database content coverage, illustrating their use with the example of evaluating databases for coverage of medical education literature. Method names: Though several methods for evaluating database content coverage have been described, the two used herein, referred to as the reference list search method and the journal method, use traditional librarian skills and tools and can be used across a variety of topic areas. Description: In this paper, we describe two approaches to evaluating database coverage. For each method, we use the example of evaluating databases for their coverage of medical education literature. In the reference list search method, we search for references from comprehensive reviews or bibliographies to determine their presence in databases. In the journal method, we describe the indexing status and coverage of key publications in databases. Strengths: These methods use established librarian skills and tools and can be applied to a wide variety of topics. Limitations: The results of each method depend on the quality and comprehensiveness of the original source material: comprehensive reviews and bibliographies for the reference list search method and publication lists for the journal method.
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.
How this classification was reachedexpand
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 | 0.155 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads 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".