2018 Consensus framework for good assessment
Bibliographic record
Abstract
Introduction: In 2010, the Ottawa Conference produced a set of consensus criteria for good assessment. These were well received and since then the working group monitored their use. As part of the 2010 report, it was recommended that consideration be given in the future to preparing similar criteria for systems of assessment. Recent developments in the field suggest that it would be timely to undertake that task and so the working group was reconvened, with changes in membership to reflect broad global representation. Methods: Consideration was given to whether the initially proposed criteria continued to be appropriate for single assessments and the group believed that they were. Consequently, we reiterate the criteria that apply to individual assessments and duplicate relevant portions of the 2010 report. Results and discussion: This paper also presents a new set of criteria that apply to systems of assessment and, recognizing the challenges of implementation, offers several issues for further consideration. Among these issues are the increasing diversity of candidates and programs, the importance of legal defensibility in high stakes assessments, globalization and the interest in portable recognition of medical training, and the interest among employers and patients in how medical education is delivered and how progression decisions are made.
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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 | 0.225 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.028 | 0.016 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".