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Record W2515792407 · doi:10.1177/0896920516658941

Community Safety, Housing Precariousness and Processes of Exclusion: An Institutional Ethnography from the Standpoints of Youth in an ‘Unsafe’ Urban Neighbourhood

2016· article· en· W2515792407 on OpenAlexafffundabout
Naomi Nichols, Jessica Braimoh

Bibliographic record

VenueCritical Sociology · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsWestern UniversityMcGill University
FundersNational Institute on AgingSocial Sciences and Humanities Research Council of Canada
KeywordsSociologySocial exclusionPrecarityParticipant observationNeighbourhood (mathematics)EthnographyNexus (standard)CriminologyOppressionQualitative researchGender studiesEconomic growthPolitical scienceSocial sciencePolitics

Abstract

fetched live from OpenAlex

Using the alternative sociological approach, institutional ethnography, this article reveals how experiences growing up in social housing (re)produce conditions of oppression that exacerbate housing precariousness and other forms of exclusion. Data were generated through participant observation, textual analysis and in-depth qualitative interviews with Young People of Colour living in vulnerable urban neighbourhoods, designated as Neighbourhood Improvement Areas in Toronto, Canada. Findings reveal how discourse, policy and practice related to community safety comprise an institutional nexus, connecting policing with social housing. These intersectional institutional relations create conditions of continuous housing precarity; youth street involvement and homelessness; increased involvement in the youth criminal justice system; and a belief among economically marginalized Young People of Colour that the state does not care about their safety and inclusion.

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)0.005
metaresearch head score (Gemma)0.004
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 score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.019
Scholarly communication0.0060.003
Open science0.0010.007
Research integrity0.0010.002
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 head0.114
GPT teacher head0.428
Teacher spread0.313 · 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

Citations23
Published2016
Admission routes3
Has abstractyes

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