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Record W4281864606 · doi:10.20429/ijsotl.2022.160204

A Collaborative Self-Study: Reflections on Convening a SoTL Community of Practice

2022· article· en· W4281864606 on OpenAlexaff
Rebecca Wilson-Mah, Jo Axe, Elizabeth Childs, Doug Hamilton, Sophia Palahicky

Bibliographic record

VenueInternational Journal for the Scholarship of Teaching and Learning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsScholarship of Teaching and LearningCommunity of practiceScholarshipDiversity (politics)Value (mathematics)PsychologyCollaborative learningSociologyPedagogyPolitical scienceComputer scienceTeaching method

Abstract

fetched live from OpenAlex

Communities of practice (CoPs) can provide opportunities for diverse and inclusive groups to convene, share, collaborate, and support others. Using a self-study research approach combined with a visual research method, this study explores both scholarly and practice-based insights to describe the anticipated attributes of a high functioning CoP for the support of collaborative engagement in Scholarship of Teaching and Learning (SoTL). The following nine emergent attributes are identified: 1) Structures; (2) Social environments; (3) Diversity; (4) Knowledge, learning and ideas; (5) Support; (6) Shared leadership; (7) Risk; (8) Results and impact; and (9) Growth over time. This study contributes to the growing body of knowledge related to the value of visual research methods in collaborative self-study. Moreover, the results of this self-study deepen understanding about the practice and role of convenors and organizers of a grass-roots, campus-wide SoTL CoP initiative.

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)0.032
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.084
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0230.027
Scholarly communication0.0110.008
Open science0.0040.017
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0040.001

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 head0.156
GPT teacher head0.524
Teacher spread0.368 · 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.

Study designQualitative
DomainEvaluation
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

Citations9
Published2022
Admission routes1
Has abstractyes

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