Governing the Drinking Driver: A Genealogical Analysis of Canadian Impaired-Driving Programmes
Bibliographic record
Abstract
Since the 1980's impaired driving behaviour has gained increased attention in the public sphere. Recently, the provincial government of Ontario has passed new measures designed to control this behaviour. By drawing on Ericson's (2007) analytic of uncertainty this thesis focusses on how risk and uncertainty have shaped the Ontario government's efforts to control impaired driving behaviour in manners that undermine the traditional "principles, standards and procedures" (Ericson, 2007: 30) of law. Through a Foucaultian genealogical analysis of both governmental and non-governmental documents pertaining to recent impaired driving control efforts including; the Road Safety Act, sobriety checkpoints, and report impaired driver initiatives, this thesis analyzes contemporary efforts to control impaired driving behaviour in Ontario from 2000 to 2012. Furthermore, by drawing on work from the larger perspective of governmentality, this thesis recommends changes to both Ericson's (2007) analytic and the governmentality perspective as a whole.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".