Time Well Spent in a Kindergarten Class:A Teacher’s Reflection on Using Talk to Learn
Bibliographic record
Abstract
Recent changes in the Ontario curricular expectations for teaching and learning have led the author to re-examine some of her teaching practices, particularly in oral language learning. In this article, the author explores what learning through talk looks like, sounds like and feels like from the kindergarten teacher’s perspective. By inquiring into her own practice, drawing from the literature on classroom talk in the early years, and critically reflecting on vignettes of classroom talk as well as a teaching journal, the author as kindergarten teacher challenges her own assumptions about talk to gain a deeper understanding of its role in a kindergarten classroom. The vignettes, reflective writing and teaching journal act as signposts that map a growing understanding of talk as a tool for learning. These stories help to ground the discussion about talk in practice as well as theory, and provide insights into the challenges and opportunities of using talk for learning in kindergarten.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".