Bibliographic record
Abstract
Presented by Alie Visser, Leanne Olson, and Samuel Cassady, Western UniversityUntil the fall of the Canadian dollar in 2016, Western University made collections decisions for journal packages based on cost per use. This was no longer adequate for the savings we needed. Our poster will explain how Western University made data-driven decisions building on the “”big deal”” analysis work initiated by the Universite de Montreal. We’ll explore:• Conducting a journal overlap analysis• Using a faculty survey to determine core titles• Performing a citation analysis of faculty publications using Web of Science and Scopus• Weighting criteria to determine potential buyback lists• Practical tools to help attendees experiment with their own collectionshttp://www.olasuperconference.ca/SC2017/wp-content/uploads/2017/01/OLA-Poster-2017-23-Dec-2016-version.pdf
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 | 0.078 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.037 | 0.052 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".