The Peel District School Board: A Leader In Supporting Its Teachers In Excellent Assessment And Evaluation
Bibliographic record
Abstract
The Peel District School Board has long been committed to helping its teachers use the best assessment practices to improve student learning and the best known evaluation strategies and tools to ensure that student progress is tracked and reported fairly and accurately. The underpinning of all assessment and evaluation in Peel classrooms is Policy #14: Student Assessment and Evaluation in Peel Elementary and Secondary Schools. Revised in 2002, it reflects the policy, rationale, principles of effective assessment, a range of assessment tasks to promote fair and inclusive assessment, suggested assessment tools, and specific guides for elementary and for secondary schools. As well expectations around the communication of student progress and a glossary of assessment and evaluation terms, it ensures that no Peel educator, student or parent is left in the dark as to expectations. Policy #14 is supported by Policy # 70, Peel's Homework policy.
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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 | 0.046 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".