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Record W2955532261 · doi:10.1111/1745-5871.12344

Campgrounds as service hubs for the marginally housed

2019· article· en· W2955532261 on OpenAlexaff
Robin Kearns, Damian Collins, Laura Bates, Elliott Serjeant

Bibliographic record

VenueGeographical Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Alberta
FundersUniversity of Auckland
KeywordsRentingService (business)GentrificationUrban agglomerationBusinessArgument (complex analysis)Space (punctuation)Scale (ratio)Rental housingEconomic growthGeographyMarketingPolitical scienceEconomic geographyEconomics

Abstract

fetched live from OpenAlex

Abstract The service hub concept is strongly associated with deprived areas of North American inner cities, where agglomerations of low‐cost housing and service providers form a space of survival for marginalised populations. In this paper, we contend that service hubs can take other forms, including as small‐scale sites of housing and service provision, informally networked across an urban region. We develop this argument with reference to suburban campgrounds in Auckland, New Zealand—a city experiencing a severe housing affordability crisis. Both individually and collectively, campgrounds enable vulnerable households, as well as tourists, to inhabit an increasingly exclusionary urban environment. Drawing on interviews with 24 resident campers and eight managers, we highlight the role of campgrounds in supporting residents through the provision of informal housing and on‐site services. This provision also benefits the facilities' owners and managers, by creating a year‐round rental income stream. We find that campgrounds are critically important for those whose lives are rendered precarious by the housing market.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.001

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 head0.165
GPT teacher head0.531
Teacher spread0.366 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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