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Record W2568571354 · doi:10.1111/1745-5871.12209

Contested geographies: competing constructions of community and efficiency in small school debates

2017· article· en· W2568571354 on OpenAlexaff
Michael Corbett, Leif Helmer

Bibliographic record

VenueGeographical Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsNova Scotia Community College
Fundersnot available
KeywordsPoliticsSpace (punctuation)Consolidation (business)SociologyClosure (psychology)Irrational numberCorporate governancePolitical economyPolitical scienceGender studiesLawManagement

Abstract

fetched live from OpenAlex

Abstract The history of rural education in North America can be understood as a history of school closure, amalgamation, and consolidation of schools. Typically, historical and geographical arguments have converged in debates about whether or not to close small community schools, which are positioned as the victims of the march of time and reconfiguration of space. In this paper, we analyse the archetypical positioning of rural parents in the ‘school wars’ as emotional and irrational participants who want to turn back time and who fail or refuse to understand and accept what is alleged to be the inevitable transformation of rural space. We then analyse the political strategy and tactics engaged in by school governance officials and community agents in these struggles over the meaning and future of rural space. We argue that the confrontational politics that ensues do not support strong rural development conversations and that the unilateral search for what we call ‘trump cards’ to settle arguments with data and rational or emotional ‘appeals’ has led to ongoing tension in rural communities.

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)0.016
metaresearch head score (Gemma)0.017
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 score0.018
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0180.115
Scholarly communication0.0160.014
Open science0.0020.011
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.120
GPT teacher head0.422
Teacher spread0.303 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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