A Critical Review of Fairness from Multiple Perspectives: Implications for Classroom Assessment Theory
Bibliographic record
Abstract
Inspired by the recent 21st century social and educational movements toward equity, diversity, and inclusion for disadvantaged groups, educational researchers have sought in conceptualizing fairness in classroom assessment contexts. These efforts have provoked promising key theoretical foundations and empirical investigations to examine fairness in assessment. This review study aims to critically review these theoretical foundations and associated empirical studies to examine their potential for addressing the complex and evolving notions of fairness in classroom assessment contexts. This study also builds on fairness and justice literature in social sciences and broader educational discourses to provide additional theoretical grounds to rethink fairness in classroom assessment. Overall, this study contributes theoretical grounds for future theory-driven empirical research to advance fair assessment practices in classrooms.
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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 | 0.050 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".