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Record W2481391747 · doi:10.15402/esj.v1i1.28

Engaged Scholarship: Reflections from a Multi-Talented, National Partnership Seeking to Strengthen Capacity for Sustainability

2015· article· en· W2481391747 on OpenAlexvenueaboutno aff
Maureen G. Reed, Hélène Godmaire, Marc-André Guertin, Dominique A. Potvin, Paivi Abernethy

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAppreciative Inquiry and Organizational Change
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipEngaged scholarshipSustainabilityGeneral partnershipPublic relationsCapacity buildingGovernment (linguistics)FacilitationWork (physics)Political scienceSociologyAppreciative inquiryPedagogyEngineering

Abstract

fetched live from OpenAlex

This paper describes a national partnership of academic researchers, government representatives, and sustainability practitioners who sought to strengthen the capacity of 16 biosphere reserve organizations working across Canada to promote sustainability through collective learning and networking strategies. We begin by situating our work within traditions of community-engaged scholarship and appreciative inquiry, and then ask participants to reflect directly on the questions. We then draw attention to four key themes: building and maintaining trust; setting clear and confirmed expectations; establishing structured and multi-lateral facilitation; and finding the sweet spot for our collective practice. Our reflections address common themes of community-engaged scholarship, including addressing cross-cultural challenges and finding joy in working together.

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)0.035
metaresearch head score (Gemma)0.065
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 score0.046
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.065
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0460.043
Scholarly communication0.0220.012
Open science0.0070.039
Research integrity0.0090.021
Insufficient payload (model declined to judge)0.0030.001

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 head0.482
GPT teacher head0.436
Teacher spread0.045 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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