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Record W1956768456 · doi:10.1111/tesg.12153

Recession Response: Cyclical Problems and Local Solutions in Northern British Columbia

2015· article· en· W1956768456 on OpenAlexafffundabout
Don Manson, Sean Markey, Laura Ryser, Greg Halseth

Bibliographic record

VenueTijdschrift voor Economische en Sociale Geografie · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsUniversity of Northern British ColumbiaSimon Fraser UniversityCoast Mountain College
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of British ColumbiaUniversity of Northern British Columbia
KeywordsRecessionBustFace (sociological concept)GeographyEconomic growthPolitical scienceEconomicsSociology

Abstract

fetched live from OpenAlex

Abstract The purpose of this paper is to trace community and regional responses to the economic downturn of 2008/2009 in Northern British Columbia, Canada. Our research during this period (2009–2011), the Northern Economic Vision II (NEV II) project, sought to investigate whether communities in the region were incorporating some of the core findings, lessons from an earlier project, the NEV I project (conducted in 2002–2005), in terms of preparing for both economic uncertainty and opportunity through place‐oriented community planning and enhanced regional collaboration. Findings suggest that in the face of economic hardship, many communities continued to pursue community and regionally‐oriented strategies in an attempt to preserve services and quality of life for local residents, and did not engage in reactive budget cuts in the face of economic decline, as experienced in previous bust periods.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.038
GPT teacher head0.229
Teacher spread0.191 · 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

Citations3
Published2015
Admission routes3
Has abstractyes

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