Bibliographic record
Abstract
The Ontario Ministry of Education has recently begun a renewed strategy for the improvement of mathematics learning and teaching, resulting in the formation of the Mathematics Knowledge Network (MKN). The MKN aims to mobilize new, evidence-based knowledge that can positively impact the conditions and outcomes of mathematics education in the province. A key component of this network is in bringing together its partners (school boards, faculties of education, professional organizations) as well as groups and individuals who are interested and involved in mathematics education. With a diverse and at times competing emphasis on skill development, knowledge generation, and community building, there are high expectations for the MKN to play many roles and offer multiple benefits for busy professionals. The goal of this research is to explore the potential challenges of forming and facilitating networks of educators, often described as communities of practice, gathered around a common goal and collective learning experience. A series of interviews and focus groups with MKN and its communities of practice leaders offer complementary data to a focused literature review, providing insight into the importance of deepening social ties between members, division of labour, and the purposeful and scaffolded innovative knowledge development.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".