The trouble with dispositions: a critical examination of personal beliefs, professional commitments and actual conduct in teacher education
Bibliographic record
Abstract
In this article, I argue that the concept of disposition is often unclear in teacher education programs, sometimes referring to general personal values and beliefs, and sometimes referring to professional commitments and actions. As a result, it is unclear whether teacher education programs should focus on selecting the right kind of person, or on educating the student for a profession. I suggest that a clearer distinction should be made between predispositions (value commitments that a person may or may not act upon) and professional dispositions (characteristics attributed to a person based on actually observed actions), and that teacher education programs should focus their attention on the latter, not the former. The question is not whether student-teachers have the ‘right’ personal beliefs but whether, if the dispositions required by the profession are at odds with their personal beliefs, the former will override the latter.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.057 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".