Bibliographic record
Abstract
I want to celebrate, along with our many volunteers and authors, the first issue of the Canadian Journal for New Scholars in Education/ Revue canadienne des jeunes chercheures et chercheurs en éducation (CJNSE/RCJCÉ).We are a bilingual journal, and a journal that embraces multiple cultures.In this volume, you will find a diverse compendium of work crafted by graduate students attending Canadian universities.This journal was first envisioned a year ago.My intent was to create a place to showcase graduate student work, to provide a publishing experience for graduate students, and to create a forum for connections and networking.We have achieved this.The CJNSE/RCJCÉ is unique in that during the review and copy editing stages of manuscript preparation authors are mentored by our experienced volunteers.Many have enjoyed and appreciated this effort.In total 44 manuscripts were submitted for the first issue.Though our acceptance rate is fairly high due to the extensive editing process, our capacity could only allow us to work closely with 15 manuscripts.The volunteer members of our editorial team, as listed on our website, have been phenomenal.With little hesitation they came forward en mass to help develop the journal and produce the first issue.Their dedication and commitment is the direct result of their willingness to support their peers and their field.My deepest thanks are extended to our Advisory Editors
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.077 | 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".