Teachers’ and other Professionals’ Learning Practices
Bibliographic record
Abstract
The purpose of this chapter is to provide an extensive comparative analysis of professional learning. As Chapter 1 has documented, professionals depend greatly on formal education entry credentials for their legitimacy. Professionals are also widely assumed to engage in continual learning to upgrade their specialized knowledge and skills to remain current in complex and changing jobs. We posit that, since professional occupations remain highly dependent on recognition of specialized knowledge, continuing participation in further job-related formal education (usually called “professional development”) is likely to be higher than in most other occupations. As noted in the Introduction, workplace learning can be seen as occurring on an informal-formal continuum, with much of it taking place informally (see Betcherman 1998: Livingstone 2009). All workers are likely to require continuing learning in relation to changing job conditions, so we expect that the incidence of job-related informal learning will be quite extensive among all occupations. Most of the attention in this chapter will be devoted to comparing formal provision of professional development for teachers and other specific professional occupations and by class positions of professionals. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".