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Record W4392721183 · doi:10.22318/icls2023.101745

A Community of Practice to Bridge Research and Practice in Science Education

2023· article· en· W4392721183 on OpenAlexaff
Caroline Cormier, Sean Hughes, Karl Laroche, Véronique Turcotte, Michael Dugdale, Kevin Lenton, Rhys Adams, Elizabeth S. Charles

Bibliographic record

VenueProceedings. · 2023
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsDawson CollegeCégep André LaurendeauVanier CollegeJohn Abbott College
Fundersnot available
KeywordsBridging (networking)Bridge (graph theory)Community of practiceLinkage (software)Knowledge managementCurriculumEngineering ethicsComputer scienceKnowledge sharingPublic relationsSociologyPolitical sciencePedagogyEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.087
metaresearch head score (Gemma)0.103
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.459

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.103
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0100.004
Science and technology studies0.0150.026
Scholarly communication0.0190.016
Open science0.0050.039
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0180.004

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.

Opus teacher head0.308
GPT teacher head0.602
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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