Bibliographic record
Abstract
In the summer of 2019, Temple University’s main library relocated to a new building, in which most of the 1.3 million-item main stacks collection resides in an automated storage and retrieval system (ASRS), and a small portion in open stacks. The open stacks, or browsing collection, includes highly circulating items, new books, and materials with a particular need for browsing. Highly-circulating items were identified by dividing the total number of loans by the number of years the library had owned the book. Materials with a particular need for browsing, generally those with significant visual components such as art and music scores, were also selected by formula, though a lower number of loans was required in order for the book to be added to the browsing title list. The Collections Analysis Librarian merged the lists of highly circulating items and highly visual items and presented the preliminary title list to Subject Specialists. These librarians then suggested categories of books that they felt should be browseable, such as maps and language dictionaries. Identifying new books was more complicated than expected, as the list needed to exclude certain categories of purchases, such as replacements or continuations, that did not belong in the open stacks. All items destined for browsing were marked with bright green stickers near the call number, which served as an effective way for the staff who packed the books to separate them from those going to the ASRS.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.109 | 0.054 |
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".