Exploring Interrogation-Related Stressors: Factors Influencing Apparent Stress in Investigative Interviews with Suspects
Bibliographic record
Abstract
Despite the potential significance of stress on individuals during investigative interviews, the examination of its general impact remains an underexplored area of research, with previous studies primarily focused on the specific phenomenon of stress-induced false confessions. As for interrogation-related stressors, they are indirectly addressed in the literature and are poorly elaborated. This article has two objectives: (1) To determine the impact associated with apparent stress on the decision of the suspects to disclose information relevant to the investigation and to confess their crimes, and (2) to determine the factors that influence the suspects’ apparent stress. The current study is based on analysis of 130 videotaped investigative interviews involving individuals convicted of offenses related to online sexual exploitation of children. The study results show that the suspects’ decisions to confess the alleged facts or to disclose information relevant to the investigation do not appear to be influenced by their apparent stress. Furthermore, the suspects’ ages and the interviewers recalling the benefits of cooperation and mentioning the desire to be honest or authentic during the investigative interview reduced the suspects’ apparent stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".