Ottawa 2020 consensus statement for programmatic assessment – 1. Agreement on the principles
Bibliographic record
Abstract
INTRODUCTION: In the Ottawa 2018 Consensus framework for good assessment, a set of criteria was presented for systems of assessment. Currently, programmatic assessment is being established in an increasing number of programmes. In this Ottawa 2020 consensus statement for programmatic assessment insights from practice and research are used to define the principles of programmatic assessment. METHODS: = 20), an inventory was completed for the perceived components, rationale, and importance of a programmatic assessment design. Input from attendees of a programmatic assessment workshop and symposium at the 2020 Ottawa conference was included. The outcome is discussed in concurrence with current theory and research. RESULTS AND DISCUSSION: Twelve principles are presented that are considered as important and recognisable facets of programmatic assessment. Overall these principles were used in the curriculum and assessment design, albeit with a range of approaches and rigor, suggesting that programmatic assessment is an achievable education and assessment model, embedded both in practice and research. Knowledge on and sharing how programmatic assessment is being operationalized may help support educators charting their own implementation journey of programmatic assessment in their respective programmes.
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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 | 0.214 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.019 | 0.013 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.013 | 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".