Clare’s Legacy Travels to Alberta: Undesired Consequences and Repercussions of Bill 17
Bibliographic record
Abstract
This paper explores the proposed legislation entitled “Clare’s Law” which will be implemented in Alberta in June of 2020. Clare’s Law has been used as a response to the alarming reported rates of domestic violence around the world. Clare Wood was a woman who was killed by her ex-partner; she contacted the police numerous times over an extended period of time but was never assisted. Clare’s Law provides individuals with the right to know and the right to ask. This will allow concerned individuals to inquire about the criminal records of one’s partner. Furthermore, it requires emergency responders to inform individuals of one’s criminal history if it pertains to domestic violence. Premier Jason Kenney is addressing the alarming reported rates of domestic violence by enacting Clare’s Law in Alberta. This paper explores the benefits and concerns surrounding the implementation of such legislation. Clare’s Law may provide individuals with awareness of their partners criminal history regarding domestic violence, however this paper’s main argument is that Clare’s Law will not be effective and will cause more harm than good. The Law does not increase services, shelters or support for victims of violence. Furthermore, it may provoke victim blaming and it assumes accountability by police services and judicial systems that have continually failed victims. This paper is significant due to the significant rate of domestic violence in Alberta. This paper states that incidences of domestic violence will not improve because of Clare’s Law and could make situations worse for victims of domestic violence.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".