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Record W4406281486 · doi:10.69520/jipe.v6i2.180

Offering Collaborative Opportunities as a Pathway to Scholarship of Teaching & Learning Research Participation

2025· article· en· W4406281486 on OpenAlexaffabout
Brendan Wehby-Malicki, M Redwan Zinan Siddiqui, Erin Gray, Francesca Discenza, Noreen Santilli, Trúc Thị Thanh Lê, Kayla Charbonneau

Bibliographic record

VenueJournal of innovation in polytechnic education. · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of Guelph-HumberHumber Polytechnic
Fundersnot available
KeywordsScholarshipScholarship of Teaching and LearningSociologyEngineering ethicsPolitical sciencePedagogyTeaching and learning centerEngineeringTeaching method

Abstract

fetched live from OpenAlex

The Scholarship of Teaching and Learning (SoTL) is the systematic study of teaching and learning and the public dissemination of findings. Despite the numerous benefits of SoTL at multiple levels, there are significant challenges to participation, particularly within the Canadian college and polytechnic context. Common barriers that contribute to lower SoTL participation in the Canadian college and polytechnic context include a lack of faculty time for scholarly activities, institutional culture and support for scholarly activities, and limited research experience among faculty members. Through a brief introduction to the literature on this topic and our own experience running a group SoTL project, we aim to shine a light on how offering collaborative opportunities can improve scholarly participation at multiple levels of engagement as well as contribute to the formation of a SoTL community of practice. By embracing a collective approach to SoTL, educators can not only develop professionally but also foster a scholarly community that encourages innovation and improves learning outcomes across diverse educational contexts.

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)0.060
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.095
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0140.009
Scholarly communication0.0200.011
Open science0.0050.049
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0140.003

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.

Opus teacher head0.177
GPT teacher head0.542
Teacher spread0.366 · 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 designQualitative
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".

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Citations1
Published2025
Admission routes2
Has abstractyes

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Same venueJournal of innovation in polytechnic education.Same topicReflective Practices in EducationFrench-language works237,207