Recognizing and interviewing suspects with intellectual disability
Bibliographic record
Abstract
Individuals with mild intellectual disabilities (ID) are overrepresented in the criminal justice system. In the Netherlands, it is estimated that one in three suspects questioned by the police has ID. Despite this high prevalence, ID often goes unrecognized by police and legal professionals, partly due to suspects’ seemingly streetwise or compliant behavior. Interviews that do not account for their cognitive limitations, such as suggestibility or a tendency to confirm questions, pose serious risks. This includes incomplete or inaccurate statements, false confessions, wrongful convictions, and lack of appropriate support. Such outcomes not only harm the suspect but also undermine the efficiency of the investigation and the fairness of legal proceedings. Empirical research on police interviewing of suspects with ID remains limited, a gap this dissertation aims to address. The primary objective was to increase understanding of both the detection process of ID in Dutch police interviews and the factors that decrease or enhance the comprehensiveness and accuracy of information provided by adult ID suspects. Drawing on a literature review and empirical data from semi-structured interviews and audio(visual) recordings of real-life Dutch police interviews, the study provides insights into current interviewing practices. This dissertation demonstrates the complexity of interviewing suspects with ID. Suspects frequently struggle to understand their rights, the questions, and the implications of their answers. A large number of suspects with ID may go unnoticed. This research highlights the positive impact of investigators' knowledge of ID prior to the interview and specialized interview training in improving interview quality. Trained investigators use clearer rights explanations, rapport-building, adjusted language, and less confrontational techniques. Open-ended questions, active listening, and the verification of information are key elements for gathering comprehensive case-related information from all suspects, including those with ID. The dissertation offers practical tools and recommendations for improving the recognition of ID and adapting interview strategies in line with legal and international standards. Real-life interview examples show how tailored approaches reduce risks and increase both the accuracy and comprehensiveness of suspect statements. These insights are directly applicable to police training and interview guidelines. By aligning Dutch practices with international standards such as the Méndez Principles, the research supports the professionalization of (inter)national police interviewing. The findings are relevant to the broader investigative practice, policy development, and academic research. Ultimately, this dissertation supports a fair legal process in which interview quality and legal protection are safeguarded for all suspects.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".