Teacher competence discourses: exposing, controlling, and punishing the ‘bad’ teacher
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".