INTRODUCTION TO THE SPECIAL ISSUE: MANUFACTURING CONSENT FOR THE PRIVATIZATION OF EDUCATION IN CANADIAN CONTEXTS
Bibliographic record
Abstract
The contributors to this special issue expose, in their respective work, the existence and dynamics of privatization in many different forms. The constitutive articles represent varied contexts (e.g., national and provincial, K – 12 and post-secondary) and focuses (e.g., social finance, school fundraising, co-op education, personalized learning, and international education), resulting in a rich and comprehensive discussion. Each contributes important insights concerning discursive practices utilized to legitimize and normalize privatization in education and the means through which public consent for the privatization of education is discursively manufactured. In different ways, these articles explore how individual and collective actions of public, private, and not-for-profit actors generate and use systems of meanings through which privatization is presented as common sense in thinking about and addressing challenges in public education. The seven articles are organized according to institutional context (post-secondary institutions and K–12 institutions). The first article spans these institutional boundaries; the next three articles focus on post-secondary education, followed by three articles centred around K–12 education.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".