Bibliographic record
Abstract
Abstract Objective – The objective of the study was to determine if summary notes or table of contents notes in catalogue records are associated with the usage of e-books in a large university library. Methods – A retrospective cohort study, analyzing titles from three major collections of e-books was employed. Titles were categorized based on the inclusion of the MARC 505 note (table of contents) or MARC 520 note (summary) in the catalogue record. The usage was based on standardized reports from 2012-2013. The measures of usage were the number of titles used and the number of sections downloaded. Statistical methods used in the analysis included correlations and odd ratios (ORs). The usage measures were stratified by publication year and subject to adjust for the effects of these factors on usage. Results – The analysis indicates that these enhancements to the catalogue record increase usage significantly and notably. The probability of an e-book with one of the catalogue record enhancements being used (as indicated by the OR) was over 80% greater than for titles lacking the enhancements, and nearly twice as high for titles with both features. The differences were greatest among the oldest and the most recently published e-books, and those in science and technology. The differences were least among the e-books published between 1998 and 2007 and those in the humanities and social sciences. Conclusion – Libraries can make their collections more accessible to users by enhancing bibliographic records with summary and table of contents notes, and by advocating for their inclusion in vendor-supplied records.
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 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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".