Self-assessment, Self-direction, and the Self-regulating Professional
Bibliographic record
Abstract
One of the cornerstones of autonomy for any profession is the claim to self-regulation. To be effectively self-regulating, the profession generally depends on the individual practitioner to self-regulate his own maintenance of competence activities. This model of individual self-regulation, in turn, depends on the practitioner's ability to self-assess gaps in competence and willingness to seek out opportunities to redress these gaps when identified. The literature relevant to these processes, however, would suggest this model of individual self-regulation is overly optimistic. We review the literature and describe several difficulties associated with the traditionally held model of individual self-regulation. In particular, research demonstrates repeatedly that 1) self-assessment is not an effective mechanism to identify areas of personal weakness and that 2) even when areas of weakness are obvious to the adult learner, we often avoid engaging in learning in these areas because such learning often takes more energy and commitment than we are willing to expend. Implications of these difficulties for the current model of self-regulation are explored.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".