Authentic assessment: from panacea to criticality
Bibliographic record
Abstract
Authentic assessment is often positioned as an educational panacea, invoked in response to a broad range of complex problems. This paper considers authentic assessment in relation to three key challenges: preparing graduates for the future, cheating, and inclusion. Despite literature supporting its potential benefits, there is limited evidence on the relationship between authentic assessment and these challenges. Through an uncritical blending of authenticity with broader educational goals, the label ‘authentic assessment’ risks becoming a distraction or a thought-terminating cliché, impeding deeper conversation and interrogation. We argue that authenticity should be considered as a set of aspirational principles within a broader pedagogical framework. Authenticity in assessment requires thoughtful and contextualised design, and the negotiation of trade-offs with other educational goals. The concept of authenticity, if used judiciously, can foster critical conversations and meaningful interrogation of educational practices, rather than serving as an oversimplified solution to complex problems.
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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 | 0.079 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.095 |
| Scholarly communication | 0.028 | 0.034 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".