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Record W2243794166

Collaborative approaches to addressing homelessness in Canada: Value and challenge in the community advisory board model

2013· article· en· W2243794166 on OpenAlexaboutno aff
Rebecca Schiff

Bibliographic record

VenueParity · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Advisory committeePolitical sciencePublic administrationCollaborative governanceCorporate governanceFace (sociological concept)Public relationsEconomic growthBusinessSociologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Over the past decade Canadian municipalities have seen the emergence of formalised systemslevel collaborative approaches to addressing homelessness and housing issues. The implementation of such approaches has been widespread and to some extent standardised based on the form of 'community advisory boards' (CABs) created by federal government through the Homelessness Partnering Secretariat. These committees have significantly affected systems-level strategic planning to address homelessness in urban, rural, and remote areas across the country. Despite significant impact and some success, these groups also face challenges related to effective collaboration and governance. There may be benefit to evaluation as well as comparative analysis at international levels, exploring the experiences and lessons of mandated systems-level homelessness collaboration across borders.

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)0.032
metaresearch head score (Gemma)0.044
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.106
Threshold uncertainty score0.772

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.044
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0380.019
Scholarly communication0.0180.006
Open science0.0060.015
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0090.001

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 head0.269
GPT teacher head0.376
Teacher spread0.107 · 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
Published2013
Admission routes1
Has abstractyes

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