Professional Knowledge “From the Field”: Enacting professional learning in the contexts of practice
Bibliographic record
Abstract
Based on a qualitative case study that examined elementary teachers’ understandings of a professional development policy, we question the conceptual disconnection between professional learning and professional practices in some conceptualizations of professional learning communities. We analyse the research data using Actor-Network Theory and report that the teachers in the case study perceived a disconnection between the scenarios of professional knowledge creation and the scenarios of professional practice. Such disconnection is exacerbated due to an ambiguous treatment of the concept of professional practice in the policy documents that endorse the idea ofprofessional learning communities. We conclude that a key element in the transformation of professional practices is the teacher’s awareness that his / her professional knowledge is enacted through his / her actions and practices, thereby concluding that professional learning is situated in the context of professional practices.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.049 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".