TOWARD THE DIFFERENTIATION OF HIGH‐CONFLICT FAMILIES: AN ANALYSIS OF SOCIAL SCIENCE RESEARCH AND CANADIAN CASE LAW
Bibliographic record
Abstract
Social science research and the courts have begun to recognize the special challenges posed by “high‐conflict” separations for children and the justice system. The use of “high conflict” terminology by social science researchers and the courts has increased dramatically over the past decade. This is an important development, but the term is often used vaguely and to characterize very different types of cases. An analysis of Canadian case law reveals that some judges are starting to differentiate between various degrees and types of high conflict. Often this judicial differentiation is implicit and occurs without full articulation of the factors that are taken into account in applying different remedies. There is a need for the development of more refined, explicit analytical concepts for the identification and differentiation of various types of high conflict cases. Empirically driven social science research can assist mental health professionals, lawyers and the courts in better understanding these cases and providing the most appropriate interventions. As a tentative scheme for differentiating cases, we propose distinguishing between high conflict cases where there is: (1) poor communication; (2) domestic violence; and (3) alienation. Further, there must be a differentiation between cases where one parent is a primary instigator for the conflict or abuse, and those where both parents bear significant responsibility.
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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 | 0.047 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.056 |
| Science and technology studies | 0.028 | 0.030 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".