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Record W2802176679 · doi:10.1111/area.12448

On thin ice: Assembling a resilient service hub

2018· article· en· W2802176679 on OpenAlexafffundabout
Joshua Evans, Damian Collins, Cher‐Ann Chai

Bibliographic record

VenueArea · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Alberta
FundersAthabasca University
KeywordsContext (archaeology)RedevelopmentService (business)GentrificationAssemblage (archaeology)Psychological resilienceGovernment (linguistics)Work (physics)SociologyResilience (materials science)BusinessPublic relationsGeographyPolitical scienceMarketingEngineeringCivil engineeringPsychologyArchaeologySocial psychology

Abstract

fetched live from OpenAlex

Inner‐city service hubs are vital spaces of survival for homeless populations. They are also vulnerable to gentrification‐induced displacement, putting the populations they serve at increased risk. This paper contributes to recent work on the resilience of inner‐city service hubs through a case study of Edmonton, Canada. Specifically, we theorise the assemblage‐like qualities of Edmonton's service hub, in the context of a major urban redevelopment project. As a heterogeneous grouping of different spaces of care, service hubs lend themselves well to assemblage thinking. Viewed from this vantage point, service hubs can been seen as relational achievements, assembled through articulations among voluntary sector, private and government organisations. We demonstrate how these articulations express multiple practices, ideals and values and add to the spatial resilience of the service hub in historically and spatially contingent ways.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.228
Threshold uncertainty score0.453

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0230.020
Scholarly communication0.0080.005
Open science0.0020.017
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.096
GPT teacher head0.448
Teacher spread0.351 · 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

Citations12
Published2018
Admission routes3
Has abstractyes

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