DIAGNOSTIC REDUCTIONISM AND THE SCIENTIFIC QUALIFICATION OF EXPERT TESTIMONY IN FAMILY LAW
Bibliographic record
Abstract
The objective of this article is to discuss the relevance of expert qualification in custody disputes, particularly in cases of alleged parental alienation, in a study with an analytical and critical approach, of a qualitative nature, using extensive bibliographic and documentary research. It examines tragic U.S. cases such as Kayden Mancuso and Aramazd “Piqui” Estevez, where forensic failures and judicial decisions disregarded abuse risks, prompting legal reforms that require specialized training, scientific methodologies, and greater accountability. The paper warns against diagnostic reductionism in assessing child rejection, a multifactorial phenomenon often oversimplified as parental manipulation, leading to false positives. Brazilian law and recent Canadian reforms demand objective proof of conduct, preventing the misuse of parental alienation claims as procedural violence. The study highlights the recurrence of rushed or inconclusive reports, frequently accepted uncritically by courts. It argues that an expert’s “proven competence” must include practical experience and continuous training. The conclusion advocates for forensic expertise that is scientifically robust, methodologically transparent, and ethically sound to safeguard the child’s best interests.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.039 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".