Bibliographic record
Abstract
Academic libraries in Canada acquire many “big deal” journal packages through a national consortium, the Canadian Research Knowledge Network (CRKN). Recently, negotiations with the American Chemical Society (ACS) broke down and it appeared that member libraries would need to negotiate with ACS individually for the Web Editions bundle of 40+ titles. It soon became clear that the University Library, University of Saskatchewan would likely no longer be able to afford the entire package, and tough decisions would need to be made. Usage data on each title were readily available – but is that enough evidence? Working under the common assumption that the primary users of this package are the Chemistry Department researchers, a citation analysis was conducted on what ACS journals these users recently published in and cited in their articles. The Chemistry Department was kept informed of developments in the ACS/CRKN situation, and expressed interest and concern in the outcome. In an effort to continue to engage chemistry researchers and offer them a voice in the process, a survey of their opinions on each ACS title was also conducted. It was hoped that combining data from these three discrete sources: usage statistics, citation analyses, and user feedback, would enable us to arrive at the most conscientious, evidence-based decisions possible. This presentation will discuss the outcome of this thorough analysis and consider the benefits and challenges of this comprehensive methodology and whether it is practical in every situation.
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 | 0.131 | 0.349 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.032 | 0.067 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.035 | 0.021 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".