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Record W2591045543 · doi:10.11634/233028791503831

Community Development and Older Men’s Programming: An International Case Study

2016· article· en· W2591045543 on OpenAlexaffabout
Kerstin Roger, Mary Anne Nurmi, Nathan J. Wilson, Corey S. Mackenzie, John L. Oliffe

Bibliographic record

VenueInternational Journal of Community Development · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British ColumbiaUniversity of Manitoba
Fundersnot available
KeywordsPhotovoiceFriendshipMentorshipCommunity developmentPerspective (graphical)GerontologyCommunity organizationPerceptionSociologyPublic relationsPsychologyPolitical scienceSocial psychologyMedical educationMedicineEconomic growth

Abstract

fetched live from OpenAlex

A growing body of research points to men’s groups as a benefit to communities because of their volunteerism and community-based programming. Spaces for older and retired men’s continued participation are provided including meaningful initiatives through these community resources. Little research, however, has explored groups for older men from a community development perspective. The purpose of this article is to describe a case study using Photovoice methodology with two men’s groups from Canada and two from Australia. We discuss men’s group participants’ perceptions of their groups’ contributions to the well-being of its members and the broader community, from a community development approach using photos as a key part of the study. Findings revealed older men’s volunteerism towards events and maintenance of community parks and museums, as well as mentorship activities, contributed to the well-being of a range of community members, while fostering a sense of accomplishment, friendship, and other benefits.

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.568
GPT teacher head0.624
Teacher spread0.056 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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