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Record W2923740196 · doi:10.1080/0969594x.2019.1593105

Conceptualising fairness in classroom assessment: exploring the value of organisational justice theory

2019· article· en· W2923740196 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAssessment in Education Principles Policy and Practice · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsQueen's University
Fundersnot available
KeywordsFoundation (evidence)Economic JusticeScholarshipValue (mathematics)Core (optical fiber)SociologyPsychologySocial psychologyEpistemologyPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.442
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it