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Record W4249698006 · doi:10.47678/cjhe.v48i1.187983

Knowledge Mobilization Practices of Educational Researchers Across Canada

2018· article· en· W4249698006 on OpenAlexafffundvenueabout
Amanda Cooper, Joelle Rodway, Robyn Read

Bibliographic record

VenueCanadian Journal of Higher Education · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsWestern UniversityMemorial University of NewfoundlandQueen's University
FundersOffice of International Science and EngineeringSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsOutreachPublishingDisseminationHigher educationIntermediaryPublic relationsSociologyLibrary scienceKnowledge managementPolitical scienceBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

Researchers are under increasing pressure to disseminate research more widely with non-academic audiences (efforts we call knowledge mobilization, KMb) and to articulate the value of their research beyond academia to broader society. This study surveyed SSHRC-funded education researchers to explore how universities are supporting researchers with these new demands. Overall, the study found that there are few supports available to researchers to assist them in KMb efforts. Even where supports do exist, they are not heavily accessed by researchers. Researchers spend less than 10% of their time on non-academic outreach. Researchers who do the highest levels of academic publishing also report the highest levels of non-academic dissemination. These findings suggest many opportunities to make improvements at individual and institutional levels. We recommend (a) leveraging intermediaries to improve KMb, (b) creating institutionally embedded KMb capacity, and (c) having funders take a leadership role in training and capacity-building.

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 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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.229
GPT teacher head0.535
Teacher spread0.306 · 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 teacher head, not a consensus.

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

Citations24
Published2018
Admission routes4
Has abstractyes

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