“I'll never play professional football” and other fallacies of self-assessment
Bibliographic record
Abstract
It is generally well accepted in health professional education that self-assessment is a key step in the continuing professional development cycle. While there has been increasing discussion in the community pertaining to whether or not professionals can indeed self-assess accurately, much of this discussion has been clouded by the fact that the term self-assessment has been used in an unfortunate and confusing variety of ways. In this article we will draw distinctions between self-assessment (an ability), self-directed assessment seeking and reflection (pedagogical strategies), and self-monitoring (immediate contextually relevant responses to environmental stimuli) in an attempt to clarify the rhetoric pertaining to each activity and provide some guidance regarding the implications that can be drawn from making these distinctions. We will further explore a source of persistence in the community's efforts to improve self-assessment despite clear findings from a large body of research that we as humans do not (and, in fact, perhaps cannot) self-assess well by describing what we call a "they not we" phenomenon. Finally, we will use this phenomenon and the distinctions previously described to advocate for a variety of research projects aimed at shedding further light on the complicated relationship between self-assessment and other forms of self-regulating professional development activities.
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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 | 0.053 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".