How to Establish and Develop Communities of Practice to Better Collaborate
Bibliographic record
Abstract
Although education research has shown collaboration to be of the utmost importance, schools continue to lack the necessary means to help them incorporate professional learning communities (PLCs) to facilitate and sustain their development and growth. We analysed the process by which PLCs were gsuccessfully implemented, under the guidance of a research-action-training initiative (R-A-T), as well as the conditions for effective collaboration between the different instances involved (school districts, university, and principals). The study was based on a conceptual framework consisting of three key concepts: (1) the PLC and two of its sub-themes, namely, participation and reification (Wenger, 1998); (2) the capital involved (economic, human, and social) (OECD, 2001; Bourdieu, 1979a; 1979b; Bourdieu et Passeron, 1970); and (3) shared leadership (Wenger, 1998). The data was from multiple sources (individual interviews, questionnaires, focus groups, personal logs). Results show that economic capital made it possible to access the human and social capitals. Economic capital in fact enabled the establishment of three PLCs which generated social capital, supported by a team of university facilitators, and ultimately human capital (material pertaining to supervision: reification).
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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 | 0.054 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".