Implicature and Question Types of Police Interrogation: An Analysis of Communication in a Theft Case
Bibliographic record
Abstract
In the context of police questioning, effective communication was essential for getting reliable information, establishing reports, detecting deception, managing emotion and compliance with legal and ethical standards. This study analysed the implication and avoidance used in the communication and question types affected by witness responses in police interrogations on a theft case in Indonesia. The study aimed to find out how implication and avoidance were used in the conversation and how question type affected witness responses. The study employed a qualitative method, using investigation reports from Indonesia as the primary data source. The study found that four maxims were used in the interrogation conversation, namely the maxim of quantity, quality, relevance, and manner and there was some communication to lead avoidance. Avoidance was found in communication 7, 9, and 10. The police interrogator employed two types of questions: open-closed and closed-ended. However, the interrogator predominantly used closed-ended questions to obtain information from the witness. Overall, this study highlighted the importance of understanding implicatures and avoidance in communication and question types in police interrogations, particularly in cases of a criminal investigation. The findings of this study may assist in developing more effective interrogation techniques that uphold the witness's rights and facilitate the attainment of accurate information.
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 | 0.017 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".