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Record W4398785802 · doi:10.15402/esj.v10i1.70875

Lessons Learned at Community-University Institute for Social Research’s (CUISR)

2024· article· en· W4398785802 on OpenAlexafffundvenueabout
Penelope C Sanz, Isobel M. Findlay

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSociologySocial researchLibrary scienceEngineering ethicsPolitical scienceEngineeringSocial scienceComputer science

Abstract

fetched live from OpenAlex

The University of Saskatchewan’s Community-University Institute for Social Research (CUISR) is celebrating its 25th anniversary very soon. Engaged Scholar Journal’s Penelope Sanz sat down with Isobel Findlay, University co-director of CUISR since 2011, to revisit the vision its founders set out 25 years ago and reflect on its achievements. The institute was established to facilitate community-university partnerships to engage in relevant and collaborative social research to gain a deeper understanding of Saskatchewan’s communities and to reveal opportunities to improve community quality of life.

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)0.038
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.483

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0110.010
Scholarly communication0.0170.013
Open science0.0040.008
Research integrity0.0080.022
Insufficient payload (model declined to judge)0.0130.006

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 head0.489
GPT teacher head0.503
Teacher spread0.014 · 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
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

Citations0
Published2024
Admission routes4
Has abstractyes

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