Breaking Up is Hard to Do – Deconstructing the Big Deal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it