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Record W2529838963 · doi:10.9707/1944-5660.1314

Knowledge as Leadership, Belonging as Community: How Canadian Community Foundations Are Using Vital Signs for Social Change

2016· article· en· W2529838963 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Foundation Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSociologyProcess (computing)Public relationsCommunity engagementSense of communityPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The concept of “community” in community foundations is being reframed – less strictly tied to the specific locales that originally defined their boundaries and increasingly about a process of engagement and a resulting sense of belonging. The greatest asset of a community foundation is not the size of its endowment, but its knowledge of community and ability to use this knowledge for positive change. This article explores the Canadian network of community foundations’ use of the reporting tool Vital Signs to implement a knowledge-driven approach to leadership and how it is using this knowledge in more inclusive, engaged models of community to drive change agendas in their own communities and, collectively, at a national scale. In implementing knowledge as a leadership tool, there remains a vast difference between what is feasible for the large community foundations and the small and new ones, particularly those in more isolated places. In spite of these constraints, community knowledge can become a means of scaling attention to particular issues and give many community foundations the confidence to frame issues in new ways.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0160.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.534
GPT teacher head0.442
Teacher spread0.092 · 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