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Record W7114784206 · doi:10.1080/02680939.2025.2600315

Teacher competence discourses: exposing, controlling, and punishing the ‘bad’ teacher

2025· article· en· W7114784206 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Education Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCompetence (human resources)Punishment (psychology)Teacher educationTeacher qualityQualitative research

Abstract

fetched live from OpenAlex

Although improving teacher competence might sound like a commonsense approach to teacher practice, we argue that the current global movements that are mobilizing teacher competence rhetoric are problematic in how it constructs the ‘good’ teacher. We conducted a critical discourse analysis of recently approved local legislation that seeks to monitor, manage, and regulate ‘bad’ teachers. Guided by poststructural theory, our analysis illustrates the ways in which teacher competence enlists discourses of instrumentalization, individualization, and deprofessionalization. The findings illustrate the effects that competence discourse has on understandings of the identities of teachers, the work of teaching, and the profession itself, arguing that competence discourses construct ‘good’ teaching as a technical practice not as an ethical and relational endeavour. Although derived from a local legislative example, the analysis is instructive for other contexts, calling attention to – and disrupting – the neoliberal reform efforts at work in/through policy texts and the effects these have on teachers and teaching.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.734
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.051
GPT teacher head0.430
Teacher spread0.378 · 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