Digital Module 33: Fairness in Classroom Assessment: Dimensions and Tensions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Perceptions of fairness are fundamental in building cooperation and trust, undermining conflicts, and gaining legitimacy in teacher‐student relationships in classroom assessment. However, perceptions of unfairness in assessment can undermine students’ mental well‐being, increase antisocial behaviors, increase psychological disengagement with learning, and threaten the belief in a fair society, fundamental to engaging in civic responsibilities. Despite the crucial role of perceived fairness in assessment, there are widespread experiences of unfairness reported by students internationally. To undermine these widespread unfair experiences, limited explicit education on promoting fairness in assessment is being delivered in graduate, preservice, and in‐service training. However, it seems that explicit education is the first step in capacity building for reducing unfair perceptions and related undesirable outcomes. The purpose of this module is thus to share the findings drawn from theoretical and empirical research from various countries to provide a space for further critical reflection on best practices in enhancing fairness in classroom assessment contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it