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The Making Research Accessible Initiative: A Case Study in Community Engagement and Collaboration

2022· article· en· W4294234185 on OpenAlexaffvenue
Aleha McCauley, Angela Towle

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConceptual frameworkRelevance (law)Community engagementKnowledge managementReciprocalWork (physics)SociologyPublic relationsProcess (computing)Community developmentConceptual modelEngineering ethicsPolitical scienceComputer scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

Recently community engagement has emerged as a priority among universities, offering new opportunities for their libraries. A literature scan of community-centred work in libraries reveals diverse examples but a lack of conceptual definitions or frameworks to help practitioners advance their work for social impact. We present a case study using the Carnegie Foundation definition of community engagement and apply two conceptual frameworks: living lab constructs and boundary spanning theory. The living lab constructs provide a framework to describe an innovation process that addresses a social challenge, experiments with specific actions for change, and defines specific returns or social impact. Boundary spanning theory provides a framework to help university leaders conceptualize linkages to community in ways that account for institutional complexity and foster reciprocal, mutually beneficial relationships with community partners. We use these two frameworks to describe the Making Research Accessible initiative which has three goals: i) increase the accessibility and impact of research done in the community; ii) increase the availability to researchers of community-generated research; iii) create opportunities for community and university members to share information and learn from each other. From the case study, we summarize what we have learned about community engagement to be of general relevance to library practitioners.

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)0.027
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.000
Scholarly communication0.0020.013
Open science0.0000.001
Research integrity0.0000.002
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 head0.278
GPT teacher head0.422
Teacher spread0.144 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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