Conversations About Education: Professional Development through a Multi-Epistemic Lens
Bibliographic record
Abstract
This study involves the program evaluation of the Conversations About Education pilot, developed as a community collaboration to offer professional development involving participants from a Faculty of Education, local school partners, and community organizations offering programming to Newcomers to Canada. The program sought to provide a collective forum where educational issues could be examined and discussed through a multi-epistemic lens. During the program events participants were encouraged to share their personal perspectives of the classroom experience, and together discuss the implications that lived/life experience has on confronting bias in teaching. The evaluation of the pilot program employed a qualitative approach involving two focus group sessions; each focus group session used semi-structured questions to explore participants’ perceptions of their experiences. The outcomes of the pilot program evaluation were found to include processes that promoted community-building, as well as the professional development of teaching professionals. This pilot program may provide an exemplar for teacher education programs to use to support similar initiatives to bring together educators in a community building activity to explore, share and learn about the world and themselves, and in this process, support critical pedagogical approaches to teaching and learning to contest cultural hegemony in classrooms and schools.
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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 | 0.038 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.039 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".