Psychological Expertise in Cases of Interparental Confl ict: Recommendations for Practice
Bibliographic record
Abstract
Forensic psychological assessment often comprises technical guidance in courts, especially in more complex matters in different areas to which psychology can provide signifi cant explanatory support.Psychological examination in court proceedings involving children in diffi cult situations of parenting confl icts requires expert technicians to have specifi c knowledge and competence to effi ciently respond to judicial demands, taking into account the best interest of the child.This article reviews some of the key aspects to be analyzed, proposing a refl ection on the areas and elements that should be considered and/or prioritized in such assessments and that raise a series of subjectivities and explorations that go beyond the domain of discourse and facts.The family dynamics, relationships and bonds between family members, expressed preferences and dislikes are dimensions that must to be analyzed for which there simple and complex underlying processes.The forensic psychological assessment should bring together the comprehensive analysis of all these aspects, combining theoretical knowledge with technical and scientifi c competence for the best interests of the child.
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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 | 0.046 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".