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Record W2076021785 · doi:10.7870/cjcmh-2009-0023

The Creation of “We Are Neighbours”: Participatory Research and Recovery

2009· article· en· W2076021785 on OpenAlexaffvenueabout
Alice De Wolff, Pedro Cabezas, Linda Chamberlain, Aldo Cianfarani, Phillip Dufresne, Peter G. Lye, Dennis Morency, Bradley Mulder, Esther Mwange, Mark Shapiro

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2009
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsWellesley InstituteUniversity of Toronto
Fundersnot available
KeywordsNeighbourhood (mathematics)Participatory action researchHarmCitizen journalismStigma (botany)Mental healthPublic relationsCommunity-based participatory researchPoliticsSociologyPsychologyPolitical scienceSocial psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Community-based participatory research is an enabling and empowering practice that is based in principles that overlap with those of mental health recovery. Using a participatory approach, an advocacy group called the Dream Team, whose members have mental health issues and live in supportive housing, planned and conducted a study of the neighbourhood impact of two supportive housing buildings in Toronto. The study found that tenants do not harm neighbourhood property values and crime rates, and that they do make important contributions to the strength of their neighbourhoods. This article demonstrates the strength of a self-directed collective of individuals who are prepared to challenge stigma and discrimination, and documents their use of participatory action research as a proactive strategy to contribute their knowledge to discussions that shape the communities, services, and politics that involve them.

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)0.211
metaresearch head score (Gemma)0.104
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.973

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2110.104
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0300.086
Scholarly communication0.0160.015
Open science0.0040.022
Research integrity0.0060.008
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 head0.582
GPT teacher head0.530
Teacher spread0.052 · 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.

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

Citations5
Published2009
Admission routes3
Has abstractyes

Explore more

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