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Record W6891775602 · doi:10.48336/vcd1-nn57

Local identities, discourses, and institutional change: an examination of voluntary municipal amalgamation in Newfoundland and Labrador

2022· article· en· W6891775602 on OpenAlexaffabout

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIdentity (music)ScholarshipGovernment (linguistics)Voluntary associationLocal governmentRhetoric

Abstract

fetched live from OpenAlex

This thesis examines the phenomenon of voluntary municipal amalgamation by observing four cases in the Canadian province of Newfoundland and Labrador. This project draws on insights from scholarship in historical and discursive institutionalisms as well as rhetoric and identity discourses to explore the question of why communities voluntarily amalgamate. The comparative case study observes four cases of amalgamation debate in rural communities of Newfoundland and Labrador: three cases (Fogo Island, Roddickton-Bide Arm, and Trinity Bay North) in which amalgamation occurred; and one case (Labrador City and Wabush) in which the communities considered and decided against amalgamation. The results support the hypotheses that 1) amalgamation is chosen voluntarily when community members believe they are facing urgent challenges that are insurmountable as a single community and 2) resistance to amalgamation is identity-driven, and may be overcome by discourses related to regional identity and community survival. Using the independent variables of regional identity discourses and concern for community survival, a framework is proposed to assist policymakers and local government scholars when assessing the likelihood of voluntary amalgamation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.243
Teacher spread0.213 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2022
Admission routes2
Has abstractyes

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