Déjà Vu, Harris and Ford: Exploring the Neoliberal Project in Ontario’s Education System Over Three Decades
Bibliographic record
Abstract
More than two decades after its introduction, neoliberal ideology has increasingly created a class and race-based gap in relation to student achievement in Ontario’s education system. Due to market-based rhetoric shaping policies and legislation, schools are increasingly encouraging students to adhere to the demands of a newly globalized world with a focus on the economy, regardless of their background. This study aims to analyze the presence of neoliberal reforms in Ontario’s education system through decisions made in government from Mike Harris’ in 1995 to the present Doug Ford administration. Specifically, I investigate how the so-called knowledge economy has produced a system that enables students deemed marketable, often from middle- and upper-class white backgrounds, and disables non-marketable students, most often the working poor and the working class, and racial and ethnic minorities, through funding cuts, heightened accountability, and standardized testing. By evaluating Ministry of Education policy documents and documents for both Conservative Premier campaigns, I analyzed the rhetoric used to introduce, consolidate and solidify neoliberal discourse throughout the past twenty years. The results showed that by simplifying education to quantifiable measures, the education system now measures concepts such as equity and inclusion in schools through standardized testing and monthly reports. Further, the rhetoric used to solidify equity and inclusion within the system focuses more on the presentation of both rather than materializing its action in schools. In order to minimize the current student achievement gap in our education system, funding needs to be focalized in social services cut by our government level to properly re“instate” the intended actions of these policies.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.040 | 0.029 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".