Bibliographic record
Abstract
In this article we reflect on some of our interactions with graduate and new scholar authors via our work as long-standing members of the current and former Editorial Board of the Canadian Journal for New Scholars in Education/ Revue canadienne des jeunes chercheures et chercheurs en éducation (CJNSE/RCJCÉ). Using our reflected experiences as a foundation, we discuss here some of the details involved in publishing a first article. The overall aim of this article is to demystify the publishing process and to inform graduate students about the required elements and processes involved in the preparation of their manuscripts and the subsequent process of publication from submission, to reviews, and editing. In turn, we hope to encourage new researchers and theorists to take the leap and publish their first articles.
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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 | 0.162 | 0.395 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.039 |
| Scholarly communication | 0.082 | 0.063 |
| Open science | 0.007 | 0.038 |
| Research integrity | 0.024 | 0.067 |
| Insufficient payload (model declined to judge) | 0.016 | 0.022 |
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".