Narrative Criminology: Understanding Stories of Crime. Edited by L. Presser and S. Sandberg (New York: New York University Press, 2015, 318 pp. £29.99 UK)
Bibliographic record
Abstract
This collection stands as a programmatic statement of what narrative criminology is, and is not. It is simultaneously an invitation to researchers to foreground narratives in their research, and an opportunity to interrogate the novelty and viability of this approach. Narrative criminology turns our attention, yet again, to stories. That we need to pay attention to stories is not a new idea—indeed, it has been a tradition in philosophy, psychology, early childhood education, sociology and other disciplines for decades. The editors of this volume, however, posit that criminologists to date have not fully embraced this narrative focus, and consequently urge us to foreground the functions and implications of individuals’ stories about crime, criminal justice and victimization, both for the storytellers themselves and the wider society. In doing so, narrative criminologists remind us that stories have a significant impact on people’s lives and future decisions. To some extent, qualitative criminologists have always used people’s stories as data. What is distinctive about narrative criminology, then, is the focus on narratives qua narratives. That is, the researcher brackets off the question of whether these stories are true or accurate, and instead focuses on them exclusively as stories. As Presser notes, ‘narrative criminologists are largely uninterested in what the world and agents in it are really like’ (Presser 2016: 139). Instead, narrative criminologists see stories as a means to gather insights into people’s understandings of social structure, human interaction, culture, subjectivities and self-identity. These could be narratives about ‘doing crime’, or, in turn, could also be narratives about desisting from crime.
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 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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".