Bibliographic record
Abstract
Welcome to the first of two special issues of the Canadian Journal of Action Research (CJAR) arising from the Teachers Learning Together (TLT) project of the Elementary Teachers’ Federation of Ontario (ETFO). Over a period of three years, beginning in 2007-2008, ETFO supported teams of teachers across Ontario who conducted action research for the purpose of improving teacher practice and student learning. Education faculty members from a number of Ontario universities provided facilitation and guidance to these teacher teams. The Ontario Ministry of Education provided the funding for the Teachers Learning Together initiative. In the first year (2007-08), the approximately 40 elementary teacher teams were granted considerable latitude in their choice of subject and topic on which to focus their action research. Five university researcher teams were each assigned several teacher teams to support during the year. In addition, they conducted case study research on some of the teacher teams with whom they worked. In the second round of TLT (2008-09), mathematics was added as the main focus, with the project otherwise maintaining the spirit of giving teacher teams substantial local decision-
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.294 | 0.154 |
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".