A Triangulation Method to Dismantling a Disciplinary "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
In late 2012, it appeared that the University Library, University of Saskatchewan would likely no longer be able to afford to subscribe to the entire American Chemical Society "Big Deal" of 36 journals. Difficult choices would need to be made regarding which titles to retain as individual subscriptions. In an effort to arrive at the most conscientious and evidence-based decisions possible, three discrete sources of data were collected and compared: full-text downloads, citation analysis of faculty publications, and user feedback. This case study will describe the triangulation method developed -- including the unconventional approach of applying a citation analysis technique to usage data and survey responses. Such a thorough, labor-intensive, method is likely not practical for analyzing larger, multidisciplinary journal bundles. When it becomes necessary to break up a smaller collection important to researchers in a particular discipline, this technique may provide strong evidence to support librarian decisions as well as involve faculty in the process. [ABSTRACT FROM AUTHOR]
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 | 0.043 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.080 | 0.293 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 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