Addressing Controversies about Experts in Disputes over Children in Canada
Bibliographic record
Abstract
There is significant controversy about the use of experts in child-related disputes in family and child protection proceedings in Canada. The 2015 Lang Review of the Motherisk Laboratory at Toronto’s Hospital for Sick Children concluded that experts retained by child protection agencies were introducing unreliable expert testimony about parental drug and alcohol use. The recent decision of Ontario Court of Appeal in M. v F suggested that evidence from a party-retained expert critiquing the opinion of a court-appointed psychologist is “rarely” helpful or admissible. This paper addresses these and related controversies about the use of experts in child-related cases. It reviews recent developments in the law governing the admissibility of expert evidence, with a particular focus on the 2015 Supreme Court of Canada decision in White Burgess, and the role of the judge as a “gatekeeper,” responsible for excluding biased or unreliable expert testimony. The paper explores the unique role played by court-appointed experts in child-related disputes. It is argued that there also should continue to be a role for experts retained by one parent to critique a report prepared by a court-appointed expert in a child-related case, though there is an obligation for party-retained experts to provide unbiased and reliable evidence, and avoid being “hired guns.” This critique role may be especially important when the state has been involved in the court process, either as a party in a child protection proceeding or by arranging for a particular court-appointed professional to undertake an assessment. It is also argued that there is a strong claim under the Canadian Charter of Rights and Freedoms that indigent parents in child protection proceedings may be entitled to a court order for funding to retain their own experts to testify to counter evidence put forward by experts funded by the government.
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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 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.043 | 0.033 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".