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Sharing SoTL Findings with Students: An Intentional Knowledge Mobilization Strategy

2021· article· en· W3134746107 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTeaching & Learning Inquiry The ISSOTL Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsMount Royal University
FundersHøgskolen i InnlandetUniversity of AdelaideUniversity of DerbyMount Royal UniversityMcMaster UniversityEmory UniversityGeorgia Southern University
KeywordsKnowledge sharingMobilizationKnowledge creationSociologyKnowledge managementPsychologyPublic relationsPolitical scienceBusinessComputer scienceMarketing

Abstract

fetched live from OpenAlex

This paper critically examines the reasons for and processes of sharing SoTL findings with students. Framed by our commitment to SoTL’s role to make teaching “community property,” we interpret sharing SoTL findings with students as an act of knowledge mobilization, where SoTL might be disseminated, translated, or co-created with the student as a legitimate knowledge broker. We connect these knowledge mobilization processes with four primary reasons why faculty might want to share SoTL findings with students. Finally, we provide examples of knowledge mobilization that use different “voices” found in contemporary communication settings and that reach various student audiences in micro, meso, macro, and mega 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.435
Teacher spread0.336 · 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