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Record W1873435875 · doi:10.22230/cjnser.2012v3n1a94

The Impact of a Community-University Collaboration: Opening the “Black Box”

2012· article· en· W1873435875 on OpenAlexafffundvenue
Lynne Siemens

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsPolitical scienceContext (archaeology)Library scienceAcademic communitySociologyService-learningPublic relationsGovernment (linguistics)HumanitiesSocial sciencePedagogyGeography

Abstract

fetched live from OpenAlex

ABSTRACTWithin the Social Economy, universities are working with community representatives to undertake research projects, service learning opportunities, and increasingly, academic program development, all with the objective of addressing social challenges. As many are quick to caution, the community is actually a sum of its various actors, interests, accountabilities and needs, which university staff and faculty must work to understand. Like the community, the university is a complex organization with politics, conflicts, tensions, and competing goals and objectives. Within this larger context, these various components, focusing on government, academic and administrative stakeholders, will impact and may even limit aspects of a collaboration between the university and its community partners. Through examination of a case study related to a graduate program, which was collaboratively developed between the university and community representatives, this article will identify and explore those accountabilities and the resulting impact on the collaboration. It will conclude withrecommendations for similar partnerships.RÉSUMÉDans l’économie sociale, les universités travaillent de pair avec des représentants de la communauté pour lancer des projets de recherche, créer des occasions d’apprentissage par le service et, de plus en plus, développer des programmes d’études universitaires; tout cela dans le but de régler des défis sociaux. Beaucoup s’empressent de formuler une mise en garde : la communauté est en réalité la somme des divers acteurs, intérêts, responsabilités et besoins qui la composent, ce que les membres du personnel et les facultés des universités doivent tenter de comprendre par leur travail. Au même titre qu’une communauté, une université est une organisation complexe constituée de politiques, de conflits, de tensions ainsi que d’objectifs concurrents. Dans ce contexte large, ces diverses composantes, en particulier les intervenants gouvernementaux, universitaires et administratifs, auront des conséquences sur la collaboration entre l’université et ses partenaires communautaires, et peuvent même en limiter certains aspects. Cette étude définit et analyse ces responsabilités et leurs conséquences sur la collaboration par le moyen d’une étude de cas liée à un programme d’études supérieures développé grâce à la collaboration de l’université et des représentants de la communauté. L’étude se termine par des recommandations visant des partenariats similaires.

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)0.026
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0330.030
Scholarly communication0.0230.020
Open science0.0030.042
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0210.002

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 head0.153
GPT teacher head0.412
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2012
Admission routes3
Has abstractyes

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