Educational Politics and Policy Change in Neoliberal Times: An Argumentative Discourse Analysis
Bibliographic record
Abstract
With the rise of neoliberal reforms and efforts to privatize education, there is a growing need to examine how actors and groups from the public and private sectors influence educational policy change together. In this article, we advance a critical approach to understanding the changing discursive space of educational politics by following discourses through an expansive policy network that goes beyond its traditional boundaries. Specifically, we draw on argumentative discourse analysis (ADA), which allows for the analysis of how and why various actors and groups come together to assign certain meanings to educational phenomena or problems, leading to policy responses or changes. Rooted in Foucault’s notions of discourse and power, ADA offers a unique approach to discourse analysis that can illuminate policy change through discourse coalitions. Three case studies from educational policy scholarship are discussed to illustrate the value and utility of ADA in future critical education policy studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".