Bibliographic record
Abstract
This paper describes how Manitoba’s Ignition Interlock Program was implemented on December 1, 2003. It describes how participation in the Ignition Interlock Program is mandatory for drivers who are granted conditional licenses following a conviction for impaired driving and for all repeat offenders or drivers convicted of impaired driving causing injury or death. Conditional licenses include explicit restrictions on the driver such as limiting the hours during which they may operate a vehicle and the purposes for which they are able to drive. Use of an ignition interlock device is an additional restriction on a conditional license for impaired drivers. In order to qualify for a conditional license, applicants must demonstrate that a full suspension would create undue hardship for them and undergo an assessment to show that they do not pose a safety risk to the public. The assessment is conducted as part of Manitoba’s Alcohol and Drug Program that has been in place since 1984. A driver charged with or convicted of an impaired driving offence is required to provide an impaired driver’s assessment prior to re-licensure. The assessment is completed by the Addictions Foundation of Manitoba (AFM). As a result of the assessment, drivers may be required to attend treatment or educational programs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".