What are the Learning Values of Grades? Exploring Grading Policies in Canada and China
Bibliographic record
Abstract
The current trend towards globalization, immigration, and internationalization of schools and universities around the world has led to the increased use of grades across educational systems. Given the use of grades for student promotion, mobilization, and admission into educational programs internationally, there is an urgent need to understand the validity of grades––the alignment of grading policies, practices, values, and consequences––within and across learning contexts. This study specifically investigates the learning value embedded within grading policies across two educational contexts – Canada and China. This analysis of Ministry of Education documents within and acros s the two learning contexts provides unique insights into grading policies from four provinces in Canada and central grading policies in China. This comparative analysis indicates significant differences in policies guiding teacher constructed grades in the two learning contexts. In Canada, achievement is the primary consideration in the construction of classroom grades, whereas grades in China include considerations of both learning and the learner. The findings of the study have implications for understanding the validity of grade interpretations of student achievement across educational systems.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".