Crime Narratives in Mandarin Police-Suspect Investigative Interview: Narrative Elements and Their Construction
Bibliographic record
Abstract
Investigative interview is the process in which suspects recount and reconstruct past events. Police officers” participation plays a vital role in the construction of crime narratives. This study, through conversational analysis of Mandarin investigative interview, scrutinizes into narrative elements involved in crime narratives and their construction. It is found that: a) narrative elements embedded in investigative interview mainly involve abstract, main action and background information and a major part of crime narrative is on background information; b) crime narrative is constructed in the interaction between police officers and suspects. Narrative elements are usually co-constructed by police officers and suspects. Suspects complete their narrative through description, evaluation and explanation, while police officers actively participate in the narrative through backchannels and questioning in various ways; c) the participation of police officers in the narrative is constrained by institutional situation and their epistemic status of crimes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".