Lipstick on a Pig? Critical Theory, Cognitive Science and Ontario’s New Language Curriculum.
Bibliographic record
Abstract
This article investigates the responsiveness of Ontario’s new 2023 Language Curriculum to recommendations from the Ontario Human Rights Commission’s (2022) Right to Read Report. The report connected many students’ failure to master reading to the Ministry and Ontario teacher training programs’ neglect of cognitive science-based approaches (CSBAs) to reading, in favour of focusing on socio-cultural issues promoted by contemporary variants of Critical Theory (CT). Arguably, those variants distract elementary educators from their primary job of teaching children to read, and are pedagogically misguided, harmful to low socio-economic students, and hostile to science and evidence. Employing mixed methods, I compare the 2023 Curriculum to the OHRC’s curriculum and pedagogy recommendations, exploring differences in terminology used in both previous (2006) and new language curricula. I find that the Ministry has not in fact supplanted the terminology of CT with that of CSBAs. While the use of CSBA terminology doubled from 2006 to 2023, there was also a 355.24% increase in CT language, including an increase of 2,233.87% in use of identity, one of CT’s prime terms. I attribute these findings to an ideological disconnect between Ontario’s educational bureaucracy and its provincial government, which I argue is itself a product of Anglo-American isomorphism and bureaucratic agenda setting. These findings mirror those from previous research into the Ministry’s decades-long adoption of CT terminology, which I suggest is hindering efforts to promote reading mastery.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".