Bibliographic record
Abstract
In the March 2002 issue of the Journal, we announced the first ever CJNR Reviewer of the Year (Gagnon, 2002). This distinction is conferred annually on one reviewer from our pool of approximately two hundred, to draw attention to and celebrate the work done by CJNR reviewers as a whole. Our standards for reviews are high and include both quality criteria and timeliness (Gagnon, 2000). Each individual in our reviewer database is assessed on several indicators in a standardized fashion, enabling us to clearly identify those individuals who stand out among others in supporting the Journal's mission. Again this year, I have the privilege of highlighting the work of one of our excellent reviewers. This year's recipient of the honour is Dr. Souraya Sidani, for her outstanding contributions during the year 2001. Dr. Sidani's reviews have been consistently thorough and detailed. She provides general comments and specific feedback. Her assessments of various aspects of manuscripts are defended with clarity, and suggestions for other approaches the author may wish to consider in re-working the manuscript are offered. References to potentially useful books and articles are often provided, as are explanations of concepts that may be incorrectly employed by the author. As for timeliness, I only wish I could be so timely _ we have received each of her reviews this year in less than 21 days! In short, I would be happy to be an author receiving a review carried out by Dr. Sidani.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".