Bibliographic record
Abstract
In this general issue (56:1), a number of articles draw attention to the oftendaunting challenges that migrants and international students face in Canadian schools and postsecondary institutions (Dodd et al; Ayoub & Zhou; Wang & Zhou).Managing transitions is likewise a common thread in this issue, and where lack of appropriate resources is a concern, for instance: with autistic students moving to secondary school (Taïeb-Lachance et al.) or Grade 12 students moving to post-secondary education (and for whom a mental literacy resource was devised in Gilham et al.), or where lack of sufficient context in understanding the challenges pre-service teachers face in their practice is identified as a primary issue (in Habak et al.).With respect to digital and online learning, Bourgeois and Ntebutse look critically at whether and how digital competencies translate from extra-curricular to within-curriculum (i.e., school), while Njingang Mbadjoin and Chaker inquire into the factors (often external) that conduce to success in distance learning (e.g, MOOCs).Fréchette-Simard et al. then review the factors (whether in public or private schools) that can contribute to student success and perseverance.For their part, Stagg-Peterson et al conducted focus groups with Indigenous ECE (Early Childhood Education) student teachers from Northern communities to hear how their post-secondary program contributed to their formation as Indigenous educators -and how it could do this more or better.Emond then asks how a teacher's awareness of body -somatic coherence -can deepen, even transform, their professional growth, with the help of mentoring and support.We conclude with a Note from the Field on research methods: participant observation.Before we move to delineating the issue's contents, we pause to introduce our six new editors, who myself Teresa Strong-Wilson and Associate 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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.097 | 0.063 |
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".