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Record W3035460588 · doi:10.29173/pandpr29395

Teacher Educators in Neoliberal Times: A Phenomenological Self-Study

2020· article· en· W3035460588 on OpenAlexvenueno aff
Magnus Levinsson, Anita Norlund, Dennis Beach

Bibliographic record

VenuePhenomenology & Practice · 2020
Typearticle
Languageen
FieldComputer Science
TopicDigital Education and Society
Canadian institutionsnot available
Fundersnot available
KeywordsNeoliberalism (international relations)Teacher educationLived experiencePedagogySociologyPhenomenology (philosophy)Resistance (ecology)JudgementAuditPolitical sciencePsychologySocial scienceManagementEpistemology

Abstract

fetched live from OpenAlex

In Sweden, and most Western countries, pervasive neoliberal policies have dramatically transformed the entire education sector in a matter of decades. As teacher educators, we have experienced how neoliberal currents have pushed Swedish teacher education towards a teacher training paradigm which may risk undermining the foundations for professional judgement. Moreover, the Bologna Process and the introduction of New Public Management have had significant consequences for what it means to be a teacher educator. In this study, we present our everyday experiences of being teacher educators, immersed in a teacher education culture in Sweden which has evolved under the pressures of neoliberalism. To address these complex lived experiences we engaged in a phenomenological first-person account. Three main themes emerged from an analysis of lived experience descriptions: (a) Alignment Slaves; (b) Audit Puppets; (c) Techno Phobes. These themes reflect different lived dimensions of being teacher educators confronted with neoliberal agendas. The paper concludes with a call for resistance to bring about change within teacher education.

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)0.008
metaresearch head score (Gemma)0.015
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0210.028
Scholarly communication0.0120.010
Open science0.0020.009
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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