An Interview with Ruth Dawson and Jane Bennett: Project Leaders for ETFO’s Teachers Learning Together
Bibliographic record
Abstract
Jane Bennett and Ruth Dawson are Staff Officers with the Elementary Teachers’ Federation of Ontario (ETFO). They both have worked on a team that developed and implemented numerous professional learning programs for elementary teachers, thanks to funding provided from the Government of Ontario. In 2007 they led an ETFO team that implemented a program of province-wide, teacher-based collaborative action research projects, identified collectively as Teachers Learning Together (TLT). During the 2007-2008 year, teachers worked in local teams to conduct action research on a topic of particular interest and relevance to the team. Teachers were given considerable latitude in their choice of topic. One of a number of university researcher teams was geographically assigned to clusters of teacher teams, to aid them in their research and with the content of the subject or topic chosen by each teacher team. Each university group also conducted case study research with some of the teacher teams to which they had been assigned. At the end of the year, each teacher and university team wrote a final report on their experience. In addition, they came together at the end in a day-long symposium of sharing.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".