On the Adequacy of Expert Teachers: From Practical Convenience to Psychological Reality
Bibliographic record
Abstract
This literature review examined approximately 10000 titles in five representative journals in education. It is conducted at two levels. Section A identified the preferred terms and metaphors to describe teachers at different expertise levels. Results indicated a great inconsistency in terms of terminology as well as definition of the same terms or metaphors in different journals, with a lot of them being suggestive and poetic. Section B started with the two most frequent terms, "expert" & "experienced", and put thirty two empirical studies into content analysis to uncover how their respective samples were operationally defined and selected. Findings showed both terms were constantly under-represented and there was a lack of dependable agreed-upon definition of "experienced and expert". It is argued our limitations in educational knowledge could be partly attributed to such poor conceptualizations, imprecise operationalization, and ‘reductive bias ‘of researchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".