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Record W2164478832

Building Community Capacity in Winnipeg's Inner City: Exploring The Learning and Resource Needs of Volunteer Boards of Directors in Non-Profit Organizations, Community Summary

2011· article· en· W2164478832 on OpenAlexaboutno aff
Lynn Skotnitsky, Evelyn Ferguson, Valerie Himkowski, Jackie Sokoliuk, Pat Wege

Bibliographic record

VenueWinnSpace (University of Winnipeg) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsPublic relationsCorporate governanceEmpowermentResource (disambiguation)SociologyExperiential learningCommunity organizationBusinessPolitical scienceEconomic growthPedagogyFinanceEconomics
DOInot available

Abstract

fetched live from OpenAlex

This report explores community development and adult education with inner city residents who sit on boards of directors of non-profit organizations - in particular licensed childcare facilities, women’s centers and family resource centers. Community organizations are important vehicles for development of neighbourhoods in terms of the degree to which they increase citizen participation, and stabilize and revitalize neighbourhoods through the creation of social capital (Gittell et al., 1999; Temkin & Rhoe, 1998). Drawing on a “community research as empowerment” framework (Ristock & Pennel, 1996), the project combined research and community capacity building by exploring the learning processes and identifying the resource needs of a sample of Winnipeg inner-city volunteers using an adult education model of experiential learning (Kolb, 1984; 1991). Data was collected through individual questionnaires, focus group/workshops and participant observation.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.606
Threshold uncertainty score0.783

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.249
Teacher spread0.179 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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