Taming Labour in Neo-Liberal Ontario: Oppositional Political Communication in a Time of “Crisis”
Bibliographic record
Abstract
This paper examines the key legitimating role of communication and the media, and the role of taming-labour, in constructing the Ontario neo-liberal hegemonic project in 1995. Media-content analysis and examination of the communication strategies of the Ontario government in the 1996 public-service strike show that the government relied on constructing the perception of a hegemonic crisis and framing labour as oppositional to the public interest of resolving the crisis. The government’s general strategy of quick-attack communications offensives curtailed media and opposition scrutiny, increasing the likelihood of policy success and media dependence on its framing of issues. A strong challenge to the government led by labour and social justice groups failed in the face of state public relations, media silence, and internal dissension. Examination of a second strike in 2002 suggests that even without a crisis, the government continued its attack on labour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".