Bibliographic record
Abstract
In conjunction with a multi-year renovation of Concordia University's main library, a comprehensive collections reconfiguration project was launched. The new library floor plans provided for increased study space and a reduced footprint for stacks. Significant deselection of physical format materials such as circulating books, reference works, government publications, and microforms was therefore necessary in order to achieve the necessary space reduction and still maintain room for growth. Although different weeding strategies were developed for specific collections and disciplines, the key factors considered were usage, currency and duplication. By focusing on reducing duplication - multiple copies, superseded editions, replication across different formats - and using data extracted from the library system, it has been possible to remove a large volume of items with minimal decision making required from subject librarians. Virtually all weeded materials have been sent to a non-profit reseller or recycled, in keeping with the university's commitment to environmental sustainability. This approach has resulted in the removal of over 60,000 duplicate copies from the monograph collection alone. At the same time access has been retained to most unique content within the collection, allaying faculty concerns about library deselection. In less than two years the original goals of space reduction for print and microform holdings have been exceeded.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".