A Common Language For Competency-Based Education
Bibliographic record
Abstract
We read with great interest the recent commentary by Rodney and Meyer entitled: “Competency-based education: The need to debunk misconceptions and develop a common language”.1 We believe that their commentary is timely, and we agree with most points made by the authors on debunking misconceptions. However, we do not share the view that there is a lack of a common definition of competency and competency-based education (CBE) itself. We also agree that this field can be confusing and that the terminology needs to be discussed, shared, and ideally unified to prevent misconceptions or inappropriate usage of vocabulary related to CBE.
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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 | 0.062 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.052 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.024 | 0.073 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".