A STUDY IN CREATING PROFESSIONAL LEARNING COMMUNITIES: TWO SCHOOLS ' EXPERIENCES
Bibliographic record
Abstract
Throughout the province of Alberta many schools are working to attain the somewhat elusive goal of becoming a "Learning Community". This study is about the journey two schools in East Central Alberta, separated by distance and a lack of cooperative tradition, undertook to become a Learning Community. It is also a study that looks at the nature of a Learning Community, and the consequences- intended and unintended- that result when school staffs engage in professional development with the learning community as an organizing principle. It is clear that professionals, working in a supportive and nurturing context, will be motivated to seek out meaningful professional development (PD) opportunities that will benefit their classrooms. Parents and students expect that teachers will network in order to seek out the PD that will create the most positive changes within their classrooms. However, trust and motivation among teachers are the essential intrinsic ingredients in the creation of a Professional Learning Community (PLC), and as the schools involved in this study have shown, they cannot be encouraged or developed overnight. These ingredients have to be slowly mixed in for the PLC model to become sustainable over the long term. Acknowledgements To my wife Tara, and my children Cora, Tristan, Tiara and Cade, thank you for encouraging me, your enduring patience with the long work hours, and the continuous support for my participation in this programme. Thank you to my parents, Wayne and Janet, for helping me realize that becoming a teacher was my calling in life.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.056 | 0.024 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".