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Record W3040788855 · doi:10.1080/02673037.2020.1789564

Moving from government to governance: addressing housing pressures during rapid industrial development in Kitimat, BC, Canada

2020· article· en· W3040788855 on OpenAlexaffabout
Laura Ryser, Greg Halseth, Sean Markey

Bibliographic record

VenueHousing Studies · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsSimon Fraser UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsCorporate governanceJurisdictionGovernment (linguistics)Local governmentAsset (computer security)BusinessAffordable housingEconomic growthEconomicsPublic administrationPolitical scienceFinance

Abstract

fetched live from OpenAlex

In resource-based communities, housing can be a contributing asset or challenge to attracting and retaining workers and families. In Kitimat, BC, Canada, a housing crisis threatened vulnerable, low income, and middle income residents during a period of rapid growth associated with renewed industrial investments. Even though housing policy and public housing provision falls under provincial government jurisdiction, the crisis response was largely mobilized by local stakeholders. Drawing upon a five year tracking study, this paper traces the rise of new governance arrangements to address local housing pressures. These governance arrangements fostered greater community awareness of housing issues; strengthened relationships across community, industry, and some senior government stakeholders; and renewed local housing assets. This collective capacity to manage housing pressures, however, remains vulnerable due to public policy incoherence that undermines or fails to adequately support local governance initiatives.

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)0.003
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 score0.109
Threshold uncertainty score0.793

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.005
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.002
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 head0.080
GPT teacher head0.236
Teacher spread0.156 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

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