Conceptualising fairness in classroom assessment: exploring the value of organisational justice theory
Bibliographic record
Abstract
Fairness has recently moved into the spotlight as a core foundation of classroom assessment (CA). However, despite its significance for high-quality CA, fairness definitions and theories have been limited in the literature. Driven by the critiques directed at the ‘inadequacy’ and ‘fuzziness’ around CA fairness and recommendations to conceptualise fairness particularly for CA contexts, this paper aims to provide an explicit definition of CA fairness. Specifically, this paper brings together current scholarship in organisational justice theory and recent findings from the CA fairness literature to offer a more thorough conceptualisation. This conceptualisation not only presents a distinction between justice and fairness, but also provides a novel discussion of the relationship between justice and fairness with consideration for potential effects on students’ learning. The paper concludes with an agenda for further research on CA fairness.
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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 | 0.053 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".