The Observed Impact of Teachers’ Capacities on Learning Community Implementation: A Participant-Observer Case Study
Bibliographic record
Abstract
I am interested in understanding how Ontario elementary teachers use their capacities to inform learning community implementation. To answer this question I conducted two sets of interviews and recorded my daily observations of 15 elementary school teachers and 4 administrators in research journals and field logs. My dissertation work was completed over a 4 month period during the 2006-2007 Academic School Year as a participant-observer in two different school districts (one Catholic and one Public School Board) in Ontario, Canada. The learning community model is purported to produce effective professional development that improves teaching and learning within schools (DuFour, 2004; DuFour & Eaker, 1998; Fullan, 1995; Louis, Kruse, & Marks, 1996; Scribner et al., 1999; Skytt, 2003). By examining how teachers’ capacities guided and sustained the process in two elementary schools in two school districts I anticipated determining some key characteristics that ultimately may provide a framework for learning community professional development implementation at the local school level.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".