Exploring the Role of Literacy Coaches: A Case Study of Three Schools in Ontario
Bibliographic record
Abstract
This paper explores the role of literacy coaches in Ontario schools. This case study uses qualitative research methods to provide a picture of what literacy coaching looks like in practice in three schools. The literacy coaches had three main roles: to act as school literacy organizers, literacy leaders, and to provide support to teachers and principals. Unlike the role of literacy coaches presented in the literature, the coaches in this study did not generally participate in observation and demonstration lessons and spent the majority of their coaching time performing organizational tasks. This study makes recommendations for future research and provides suggestions for school boards implementing literacy coaching. Cet article explore le rôle des formateurs en alphabétisation dans les écoles de l’Ontario. Cette étude de cas repose sur des méthodes de recherche qualitatives et offre un aperçu pratique de la formation en alphabétisation dans trois écoles. Les formateurs en alphabétisation jouent trois rôles principaux : agir comme organisateurs de l’alphabétisation dans l’école, être chefs de file en alphabétisation et appuyer les enseignants et les directeurs. Contrairement aux formateurs présentés dans la documentation, ceux dans cette étude n’avaient pas tendance à participer aux observations et aux leçons pratiques. La plupart de leur temps était passé à accomplir des tâches organisationnelles. Cette étude offre des recommandations quant aux études à l’avenir et des suggestions pour les conseils scolaires en matière de la mise en œuvre de la formation en alphabétisation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".