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Record W2618250290 · doi:10.1177/0020872817706406

Understanding the meta-discourse driving homeless policies and programs in Toronto, Canada: The neoliberal management of social service delivery

2017· article· en· W2618250290 on OpenAlexaffabout
Marjorie Johnstone, Eunjung Lee, Jo Connelly

Bibliographic record

VenueInternational Social Work · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsNeoliberalism (international relations)SociologyIdeologySocial workService delivery frameworkPublic relationsCritical discourse analysisSociocultural evolutionService (business)Public administrationEconomic growthPolitical sciencePolitical economyPoliticsEconomicsEconomy

Abstract

fetched live from OpenAlex

Although Canada is one of the top 10 trading nations in the global economy, homelessness continues to be a significant social problem. To understand this paradox, we examine academic debates on best practices, the genesis and sociocultural contexts of recent policies, and community-based homeless services. Our critical review unveils the underlying ideology of service provision: the meta-discourse of neoliberalism, which results in time-limited programs, divorced from community needs, with an ad hoc development of services. We resist this discourse by highlighting how a social work commitment to social justice can mitigate the new neoliberal requirements in homeless services delivery.

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)0.021
metaresearch head score (Gemma)0.024
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: none
Teacher disagreement score0.337
Threshold uncertainty score0.769

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.013
Science and technology studies0.0270.056
Scholarly communication0.0230.008
Open science0.0040.007
Research integrity0.0040.008
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 head0.178
GPT teacher head0.417
Teacher spread0.240 · 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

Citations19
Published2017
Admission routes2
Has abstractyes

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