Canada's Democracy Week: Let's Talk Teacher Needs
Bibliographic record
Abstract
This presentation will focus on a project entitled, Canada's Democracy Week: Let's Talk Teacher Needs. This study allowed for interactive discussions with 100 pre-service teachers from the University of Ottawa faculty of education, and 20 in-service teachers from Ottawa school boards, as well as subject-matter experts in civic education and youth engagement. The event foucsed on the best practices in teaching civics, available recources and tools, and how to build teacher confidence. Our use of digital surveys, roundtable discussions, and follow-up surveys increased our undertansing of knowledge mobilization, our awareness and understanding of best practices in teaching civics, available resouces and tools, and how to build teacher confidence. It also acted as an avenue to generate feeback on what teachers need to teach civics, and what pre-service teachers neede to feel prepared to teach civics, including professional development, programs and knowledge.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.054 | 0.007 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".