Criteria for good assessment: Consensus statement and recommendations from the Ottawa 2010 Conference
Bibliographic record
Abstract
In this article, we outline criteria for good assessment that include: (1) validity or coherence, (2) reproducibility or consistency, (3) equivalence, (4) feasibility, (5) educational effect, (6) catalytic effect, and (7) acceptability. Many of the criteria have been described before and we continue to support their importance here. However, we place particular emphasis on the catalytic effect of the assessment, which is whether the assessment provides results and feedback in a fashion that creates, enhances, and supports education. These criteria do not apply equally well to all situations. Consequently, we discuss how the purpose of the test (summative versus formative) and the perspectives of stakeholders (examinees, patients, teachers-educational institutions, healthcare system, and regulators) influence the importance of the criteria. Finally, we offer a series of practice points as well as next steps that should be taken with the criteria. Specifically, we recommend that the criteria be expanded or modified to take account of: (1) the perspectives of patients and the public, (2) the intimate relationship between assessment, feedback, and continued learning, (3) systems of assessment, and (4) accreditation systems.
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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 | 0.460 | 0.486 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.034 | 0.027 |
| Research integrity | 0.033 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".