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Record W2102128398 · doi:10.18061/dsq.v28i1.68

Shared Values, Networks, and Trust among Canadian Consumer-Driven Disability Organizations

2008· article· en· W2102128398 on OpenAlexaffabout
Susan Arai, Peggy Hutchison, Alison Pedlar, J. R. Lord, Val Sheppard

Bibliographic record

VenueDisability Studies Quarterly · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsRegional Municipality of WaterlooBrock UniversityMinistry of Tourism, Sport and the ArtsUniversity of Waterloo
Fundersnot available
KeywordsSocial capitalWelfarePoliticsSocial securityWelfare stateMental healthPublic relationsState (computer science)BusinessPolitical sciencePsychology

Abstract

fetched live from OpenAlex

This article focuses on the development of social capital among consumer-driven disability organizations in Canada. A new social movement focuses on issues of identity, quality of life and the lifestyle of people within the movement rather than solely on rights, income security and provisions of the welfare state. Reported here are survey findings revealing the network and values that form the relationship between four national consumer-driven disability organizations (Council of Canadians with Disabilities, the Canadian Association of Independent Living Centres, People First of Canada, and the National Network for Mental Health) and their member or affiliate organizations. Study results reveal features within the new social movement that contribute to, and diminish, social capital, including issues around the development of shared values, establishment of networks and supports within an atmosphere of trust and mutuality. Study findings expand on the mobilizing and political capacity found among consumer-driven disability organizations in Canada.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.073
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0180.007
Scholarly communication0.0060.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.289
Teacher spread0.261 · 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

Citations4
Published2008
Admission routes2
Has abstractyes

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