A Community of Practice to Bridge Research and Practice in Science Education
Bibliographic record
Abstract
Communities of practice (CoPs) have been used to support practitioners' efforts to adopt new teaching methods.In this paper, we summarize how our team facilitated knowledge transfer by forming and leveraging several CoPs that shared the common objective of implementing Inquiry-Based Labs (IBL) in science curricula.Over two years, our team members played the role of linkage agents in the CoPs to bridge the gap between education research, by sharing our own research findings, and practice, by collecting feedback directly from IBL practitioners about their challenges with implementation.As various needs of the members were well metto be informed, to share thoughts, to belongthe CoPs have since evolved into stable, sustainable entities.Through these powerful social interactions, CoP members themselves have become linkage agents, connecting us to the larger community that would otherwise not engage with our research and thus further bridging the gap between research and practice.
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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 | 0.087 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.005 | 0.039 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".