Producing “Good” Citizens: A Critical Discourse Analysis of the Ontario Ministry of Education’s Publication, Achieving excellence
Bibliographic record
Abstract
Researchers are concerned that education is increasingly pressured and influenced by market-driven agendas which may compromise or usurp education as a vehicle for social justice and critical thinking.In this study, I employ Critical Discourse Analysis (CDA) as a theoretical framework and research method to examine the discursive construction of education in the Ontario Ministry of Education's (OME) 2014 publication entitled, Achieving excellence: A renewed vision for education in Ontario (hereafter referred to as Achieving excellence).I use Fairclough's three dimensional framework to look at metaphors, lexical choices, and multimodal features within the text.My findings suggest that Achieving excellence discursively constructs education as a path to employment and connects employment to good citizenship.This appears to be a trend within neoliberal society, which requires further critical study.create a balance in my work.Dr. Sheyholislami provided detailed feedback and wise academic advice.Without knowing it, Dr. Fox's words have picked me up and dusted me off many times.Thanks are also due to the superb faculty in this department in general, and more specifically to Joan Grant for always living up to the "Joan Knows" folklore, and to Natasha Artemeva for planting the seed all those years ago.I would like to express my gratitude to both of my parents.My Mom for listening, reading drafts, and printing and mailing me endless articles and book chapters as well as for understanding and encouraging my passion.My Dad for his sense of humour, encouragement, and his contagious pride in me.Thank you to my extended and enormous family for always supporting me.My grandparents (
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.028 | 0.037 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".