A New Way of Teaching Criminology for Investigation and Trial: A Narrative-Based Approach
Bibliographic record
Abstract
Abstract The narrative-based approach acts as the only tool capable of creating and assigning a meaning to individual life stories, linking individuals to their actions. The use of narrative as a reference frame for understanding the motive of the crime therefore offers an innovative perspective into criminology and its forensic application. Through the stories of the criminals and the victims, of society, and the world of justice as a whole, doing narrative criminology means listening to and accurately analysing criminal life stories to shed some light and meaning on the obscure elements of reality that from time to time take shape as a violent act. After a review of the most recent literature in the criminological narrative area, the present work analyses the role of the criminologist as an expert who provides an essential contribution during investigation and trial phases. Moreover, the work proposes the use of a narrative approach and the contribution of a narrative criminologist in two different moments of the criminal procedure: during the investigation phase, through a preventive methodological narrative training of forensic experts, with emphasis on team work, and in the trial phase through the use of criminological interviews to assess criminal liability and dangerousness.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".