INTRODUCTION TO SPECIAL ISSUE
Bibliographic record
Abstract
This special issue aims to explore Canadian pedagogical and curricular practices in child and youth care and youth work preservice education with an emphasis on empirical and applied studies that centre students’ perspectives of learning. The issue includes a theoretical reflection and empirical studies with students, educators, and practitioners from a range of postsecondary programs in Quebec, Ontario, Alberta, and British Columbia. The empirical articles use various methodologies to explore pedagogical and curricular approaches, including Indigenous land- and water-based pedagogies, ethical settler frontline and teaching practices, the pedagogy of the lightning talk, novel-based pedagogy, situated learning, suicide prevention education, and simulation-based teaching. These advance our understanding of accountability and commitment to Indigenous, decolonial, critical, experiential, and participatory praxis in child and youth care postsecondary education. In expanding the state of knowledge about teaching and learning in child and youth care, we also aspire to validate interdisciplinary ways of learning and knowing, and to spark interest in future research that recognizes the need for education to be ethical, critically engaged, creatively experiential, and deeply culturally and environmentally relevant.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.344 | 0.193 |
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".