Principles for Fair Student Assessment Practices for Education in Canada The Principles for Fair Student Assessment Practices for Education in Canada contains a set of
Bibliographic record
Abstract
principles and related guidelines generally accepted by professional organizations as indicative of fair assessment practice within the Canadian educational context. Assessments depend on professional judgment; the principles and related guidelines presented in this document identify the issues to consider in exercising this professional judgment and in striving for the fair and equitable assessment of all students. Assessment is broadly defined in the Principles as the process of collecting and interpreting information that can be used (i) to inform students, and their parents/guardians where applicable, about the progress they are making toward attaining the knowledge, skills, attitudes, and behaviors to be learned or acquired, and (ii) to inform the various personnel who make educational decisions (instructional, diagnostic, placement, promotion, graduation, curriculum planning, program development, policy) about students. Principles and related guidelines are set out for both developers and users of assessments. Developers include people who construct assessment methods and people who set policies for particular assessment programs. Users include people who select and administer assessment methods, commission assessment development services, or make decisions on the basis of assessment results and findings. The
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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 | 0.112 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".