Article Usage Analytics for the Canadian Journal of Communication 2015–2018: A Guide for Authors, Publishers, and Readers
Bibliographic record
Abstract
Background The historical context for this article is the embrace by the Canadian Journal of Communication of emerging technology and, most recently, article analytics. Analysis The article focuses on four years of usage data for articles published since the 1974 founding of the CJC. It is intended to assist the journal, authors, and readers in understanding the potential for usage of articles published in CJC. The article details overall usage; explores journal penetration of global, national, and special markets; depicts the range of subject matter published; outlines the performance of the journal website and two secondary aggregators; summarizes usage levels throughout the journal’s collection; and reports on the year of publication and age of article data. Conclusions and implications The details of the findings provide insight into the nature of usage of articles in the field. In general, but of less interest than the detailed findings, one can surmise that usage is influenced by subject matter, presentation style, editorial vision, an initial quick rise to peak usage, access dynamics, but not authorship in and of itself.
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 | 0.016 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.056 | 0.054 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.036 |
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".