The Development of a Logic Model for the Protection against Family Violence Act: An Incremental Approach
Bibliographic record
Abstract
On 1 April 2005 the Northwest Territories (NWT) became the seventh jurisdiction in Canada to implement family violence legislation. The NWT Protection Against Family Violence Act (PAFVA) is civil legislation designed to protect victims of family violence, improve access to the justice system, and provide a wider range of remedies than were available under existing civil legislation. It also provides a proactive framework that can be used when the grounds to lay a criminal charge may not exist. This article describes a process that was followed during the implementation period to clarify the intentions and delivery of the PAFVA program. This process involved the iterative development of a logic model. The process of development had two unique features; first it involved the clarification of a program from legislation, and second, it involved an incremental approach to logic development. Despite attention given to program clarification in the evaluation literature, there is little information on how to handle the translation of legislation through such processes. Thus, this paper is a contribution to this gap in evaluation knowledge by detailing the process followed and the lessons learned. The following section presents a contextual overview of the PAFVA.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".