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Record W4399452082 · doi:10.55016/ojs/ajer.v60i4.55980

Education Reform: The Effects of School Consolidation on Teachers and Teaching

2015· article· en· W4399452082 on OpenAlexaffvenue
Barbara Barter

Bibliographic record

VenueAlberta Journal of Educational Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsConsolidation (business)RestructuringAccountabilitySociologyPedagogyArgument (complex analysis)Education reformPolitical sciencePublic administrationPrimary educationEconomicsMedicineLaw

Abstract

fetched live from OpenAlex

At least as early as the nineteenth century, the two most related approaches to societal improvement have been restructuring and systemic reform. For education, that has meant school closures as well as consolidation of schools and school districts. Although there exists a substantive literature on educational reform there appears to be little discussion on the impact such actions have on schools. This paper briefly describes research on current issues in rural education with a focus on consolidation. The methodology emphasizes teachers’ knowledge of practice and is followed by the findings, which highlight the impact these reforms have on practicing teachers. The argument is made that government reforms have adopted consolidation of schools and districts as a primary strategy for fiscal accountability. This focus singles out economics, while excluding the cultural and social context embedded within schools and central to the communities in which the schools are situated. In this paper, the argument put forth is that consolidation as a single-minded strategy of education reform is an inadequate framework that places teachers’ work and student learning at risk. The accounts of practicing educators speak for using consolidation only where absolutely necessary and maintaining as many small schools as possible.

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)0.005
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.118
GPT teacher head0.495
Teacher spread0.376 · 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.

Study designTheoretical or conceptual
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

Citations5
Published2015
Admission routes2
Has abstractyes

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