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Record W2253891593 · doi:10.29173/cjfy27142

The Effects of Neighbourhood, Community, and Social Networks on Marginalized Youths’ Well-being: An Arts-based Approach

2016· article· en· W2253891593 on OpenAlexafffundvenueabout
Kevin de Leon, Lynda M. Ashbourne, Jane Robson

Bibliographic record

VenueCanadian Journal of Family and Youth / Le Journal Canadien de Famille et de la Jeunesse · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health Research
KeywordsPhotovoiceNeighbourhood (mathematics)FeelingThe artsSociologyPovertySense of communityFocus groupSocial psychologyParticipant observationCollective efficacyGender studiesPsychologySocial sciencePolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

Action research and arts-based activities were used to investigate the experiences of youths, ages 16-20 years, in the Guelph community who identified as being socially marginalized through poverty and/or unstable housing. The focus of the group was on identifying the influences of structural violence in their lives. As part of their discussions, they identified the ways in which their personal safety and well-being, their sense of feeling comfortable and included in the broader community, and their presence and role within this community were influenced by the ways others in their neighbourhoods and social networks treated them. In particular, they described the assumptions and treatment by others that were based on classism and ageism as excluding them and threatening their feelings of safety and well-being when living on the street and/or receiving social assistance. The youth group expressed these ideas through discussion, photovoice, and drawing their version of a ‘community map.’ This paper includes examples of these participant-produced arts projects to demonstrate their observations and ideas.

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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.149
GPT teacher head0.442
Teacher spread0.293 · 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

Citations0
Published2016
Admission routes4
Has abstractyes

Explore more

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