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Record W2428149867 · doi:10.15402/esj.v1i2.108

Measuring Capacity for Community-Engaged Scholarship: Results from an Institutional Self-Assessment at the University of Saskatchewan

2016· article· en· W2428149867 on OpenAlexaffvenueabout
Jethro Cheng, Nazeem Muhajarine, Linda M. McMullen, Andrew Dunlop

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsScholarshipOutreachGeneral partnershipEngaged scholarshipSociologyPublic relationsCommunity engagementPolitical scienceMedical educationMedicine

Abstract

fetched live from OpenAlex

The University of Saskatchewan conducted an institutional self-assessment survey in the fall of 2013 and winter of 2014 to measure its capacity for community-engaged scholarship. This effort is part of a national initiative of eight Canadian universities (Community-Engaged Scholarship Partnership), working to change institutional policies and practices around community-engaged scholarship. This paper reports on the results of the University of Saskatchewan’s self-assessment survey completed by 159 participants across campus that include administrators, faculty, and professional staff. The participants report that there are strong practices of community-engaged scholarship throughout the University. However, there are also many opportunities to strengthen the support and capacity for community-engaged scholarship. Institutional leadership and support, for example, that is consistent and effective is required at multiple levels (department, college or school, university) in order for community-engaged scholarship to be recognized and rewarded in all academic processes. The University’s Community Engagement and Outreach Office at Station 20 West is one notable exemplar of community-engaged scholarship and practice; it is a good example of how students, faculty, and community are effectively supported in these activities.

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)0.903
metaresearch head score (Gemma)0.478
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9030.478
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.7850.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0000.613
Insufficient payload (model declined to judge)0.0000.000

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 head0.214
GPT teacher head0.376
Teacher spread0.162 · 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; both teacher heads agree on what is shown here.

Study designQualitative
DomainMethods
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
Published2016
Admission routes3
Has abstractyes

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