Comparing the Use of Books with Enhanced Records versus Those Without Enhancements: Methodology Leads to Questionable Conclusions
Bibliographic record
Abstract
A review of: Madarash-Hill, Cherie and J.B. Hill. “Electronically Enriched Enhancements in Catalog Records: A Use Study of Books Described on Records With URL Enhancements Versus Those Without.” Technical Services Quarterly 23.2 (2005): 19-31. Abstract Objective – To compare the use of books described by catalogue records that are enhanced with URL links to such information as dust jackets, tables of contents, sample text, and publishers’ descriptions, with the use of books described by records that are not enhanced with such links. Design – Use study. Setting – Academic library (Southeastern Louisiana University, Sims Memorial Library). Subjects – 180 records with enhancements and 180 records (different titles) without enhancements. Methods – The study identified the sample of unenhanced records by conducting searches of the broad subject terms “History”, “United States”, “Education”, and “Social” and limiting the searches to books. The enhanced sample was derived in the same manner, but with additional search limiters to identify only those records that had URL enhancements. An equal sample of enhanced and unenhanced records (50 or 30 of each) was tracked for each of four search terms. Only records for books that could be checked out were included, as use statistics were based on whether or not a book was borrowed. While half of the enhanced records had full-text elements (such as descriptions) that were indexed and thus searchable, the rate of use for these records was not tracked separately from the enhanced records that only had URL enhancements. Main results – Books described on records with URL enhancements for publisher descriptions, tables of contents, book reviews, or sample text had higher use than those without URL enhancements. Only 7% of titles with URLs, compared with 21% of those without, had not been borrowed. 74.67% of titles with URLs had been checked out one or two times, compared with 69.5% of those without URLs. The number of titles with enhanced records that had 3 or more checkouts was almost double the rate of unenhanced titles (18% to 9.5%). Conclusion – The authors conclude that catalogue records that have electronic links to book reviews, cover jackets, tables of contents, or publisher descriptions can lead to higher use of books, particularly if textual enhancements such as descriptions are also searchable.
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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 | 0.435 | 0.742 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".