The Operationalization of the Language Curriculum in Half-day and Full-day Kindergarten
Bibliographic record
Abstract
This thesis describes a qualitative case study that examined the intended and operationalized kindergarten Language curriculum in one Ontario half-day kindergarten classroomandonefull-daykindergartenclassroom. Theresearchquestionswere:What are the components o f an operationalized curriculum in a half-day and full-day Ontario kindergarten program? How did each operationalized curriculum differ from the intended curriculum as outlined in the teacher’s day plans? How did curricula vary across program types? I also asked teachers to comment on challenges encountered as they operationalized their intended Language curriculum. The analysis drew on observational field notes, interviews with two kindergarten teachers, and day plans prepared by the teachers. Data were categorized using the research questions as expected themes and again using Schwab’s five curriculum commonplaces. Both classrooms were culturally and linguistically diverse, but the needs of English language learners were not explicitlyaddressedintheprogramdocumentsorthecurricula. Eachteacherfeltthatshe was able to address all the Language expectations in the two-year program, but the teacher in the half-day program felt challenged to provide support to individual children and was able to schedule little time for social interaction and child-initiated play-based learning. The data suggests that the full-day program afforded more time to address the Language expectations in authentic ways, but having an Early Childhood Educator (ECE) as a teaching partner made a more noticeable difference to the curriculum than having more time.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".