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Record W2794077581 · doi:10.4000/echogeo.15218

Restructuring of Rural Governance in a Rapidly Growing Resource Town: The Case of Kitimat, BC, Canada

2018· article· en· W2794077581 on OpenAlexaffabout
Laura Ryser, Greg Halseth, Sean Markey

Bibliographic record

VenueEchoGéo · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsSimon Fraser UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsRestructuringCorporate governanceContext (archaeology)Government (linguistics)Abandonment (legal)Economic growthPoliticsPublic policyResource (disambiguation)State (computer science)Political sciencePublic administrationBusinessEconomicsGeography

Abstract

fetched live from OpenAlex

A new era of industrial development is unfolding in resource-dependent regions. In Canada, the local government context in these regions, however, is very different now than when industrial resource development expanded in the 1950s and 1960s. Drawing upon our case study in Kitimat, British Columbia, we highlight transformations associated with neoliberal policies that have affected rural governance. Neoliberal public policy shifts include wider changes where the state has become less involved in program and infrastructure investments in resource-dependent communities. Even as this political economy continues to evolve, past neoliberal policy responses continue to restrict local supports, while also failing to provide a comprehensive strategy to guide rapidly changing communities. In Kitimat, this has prompted a variety of responses emblematic of a shift from government to governance. The town has become more entrepreneurial and innovative to strengthen and diversify their economy. The abandonment of top-down policy levers has prompted community groups to pursue a greater voice in decision-making by opening up public participation in new planning and development processes. While new rural governance arrangements have provided positive and proactive contributions to emerging pressures, concerns persist about the long-term viability of these structures without a renewed vision, and accompanying policy, from senior governments to support rural communities and regions.

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 score0.110
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0350.009
Scholarly communication0.0050.001
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.193
Teacher spread0.187 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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