Three Case Studies of the Language Used to Justify Recent Neoliberal and Neoconservative Curricular Reform
Bibliographic record
Abstract
The overarching objective of this study is to become more closely attuned to the politics of curriculum by identifying the discursive practices employed by governments to position curricular reform. In particular, this analysis aims to show how the twinning of neoliberalism and neoconservatism has served to justify shifts in curriculum at three North American sites in recent years. Further, using rhetorical analysis as a form of critical discourse analysis, the study demonstrates how discursive tools are used to advance neoliberal and neoconservative values under the guise of a taken-for-granted sense of education’s purpose and role. Rather than an analysis of curriculum documents as texts, this study focuses on government rhetoric describing the rationale for curricular reform so as to better recognize which values are gaining formal power, offer clarity into what is oppressed or ignored, and, ultimately, provide insights into where resistance might be aimed.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".