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Record W1728926381

Critical Education and Insurgent Pedagogies: An Interview with E. Wayne Ross

2015· article· en· W1728926381 on OpenAlexvenueno aff
Carlo Fanelli

Bibliographic record

VenueAlternate routes · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsNeoliberalism (international relations)IdeologyCommonsPolitical sciencePublic administrationGovernment (linguistics)PoliticsSociologyPublic educationEconomic growthPolitical economyEconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

Carlo Fanelli (CF): Before working in the post-secondary education sector, you also taught as a pre-school and high-school instructor. Could you explain the impact that neoliberalism has had philosophically and as a political economic project on the institutional aspects of education. Have there been noticeable cultural shifts, diferences in pedagogical emphases or allocation of funding priorities? E. Wayne Ross (EWR): For more than three decades now there has been a steady intensiication of education reforms worldwide aimed at making public schools and universities more responsive to the interests of capital than ever before. And, neoliberal ideology is at the heart of what’s been labelled the global education reform movement or GERM. Key neoliberal principles such as reducing government spending for education (and other social services) and privatizing public enterprises has led to targeting the very existence of public education or more precisely education in the public interest. Indeed, a key aim of neoliberalism is the destruction of the commons, the very idea of the common

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)0.010
metaresearch head score (Gemma)0.019
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: none
Teacher disagreement score0.033
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0200.020
Scholarly communication0.0060.012
Open science0.0020.005
Research integrity0.0110.019
Insufficient payload (model declined to judge)0.0050.001

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 head0.084
GPT teacher head0.417
Teacher spread0.332 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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