An Introduction to the Canadian Association of Learned Journals Readership Analytics Project’s Compiled Online Journal Usage Software
Bibliographic record
Abstract
Background: This technical paper contains written versions of the texts that accompany a set of five slide-based movies that provide instruction and reviews the compiled analysis generated by the software developed by the Canadian Association of Learned Journals Readership Analytics Project. Analysis: First come usage instructions. Next are the second and third movies that walk the listener/reader through a case study-based summary of the Standard and Premium Reports. Fourth is a multi-year analysis of the case-study data. Fifth are some observations and insights. Observations and insights: The data provide a foundation for a detailed understanding journal usage. At a second level, the data point to ongoing growth in usage at less than five cents per full text article view by users in an environment in which lowering acquisition costs and declining library subscriptions predominate. The data also show widespread use throughout the collection of articles that the journal has brought forward over its 40-plus years of operation. Finally, the data suggest a number of article profiles that may assist in understanding usage. Keywords Journal metrics; Online journal usage; Journal publishing; Open access; Data visualization; Scholarly Communication.
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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.083 | 0.056 |
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".