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Record W2896727009

Neoliberalism Meets the Neighbourhood: Marginalized Communities, School Closures, and our Responsibilities as Teacher Educators

2018· article· en· W2896727009 on OpenAlexaffabout
Ruth Kane

Bibliographic record

Venue2018 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTechnocracyNeoliberalism (international relations)Neighbourhood (mathematics)SociologyPoliticsSchool choicePublic relationsCommunity engagementPolitical scienceClosure (psychology)Public administrationPedagogySocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

This paper addresses the role of neoliberalism in the closure of an urban community high school in Ontario. As researchers and teacher educators, we had worked in this community for a number of years, and had an abundance of research evidence indicating the valuable role this school played in meeting the needs of its diverse and underprivileged neighbourhood. However, the decision to close the school was justified based on arguments that doing so would better meet the needs of students. As this paper illustrates, this decision was made using narrow and technocratic criteria at the school board level, within a neoliberal policy framework set out by the Ontario Ministry of Education. While community members provided many impassioned and articulate arguments against closing their school, their perspectives were not accepted as valid by the school board. In addressing these circumstances, this paper answers two research questions: 1) What were the political, policy, and contextual factors that led to the decision to close this urban community school? 2) In what ways might we have better leveraged our position as academic researchers and teacher educators involved in the community in order to fight the closure of this school?

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.059
GPT teacher head0.335
Teacher spread0.276 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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