The contemporary fictional police detective as critical security analyst: Insecurity and immigration in the novels of Henning Mankell and Andrea Camilleri
Bibliographic record
Abstract
Abstract This article argues that popular culture can make an important contribution to security studies, and especially critical security studies, either by offering alternative narratives that challenge the dominant view of security or by deconstructing the process of securitization. It then examines a genre of popular culture that until now has been largely ignored by security studies, noir police procedural novels, and shows how the contemporary police detective can no longer be viewed in the same way as the defender of the status quo depicted in the traditional police procedural. At the same time, he or she does not correspond to the portrait of the security professional, a member of those forces that contribute to the way security is officially defined. He or she has become, instead, a critic of prevailing security practices. To illustrate how this has happened, the article takes novels from two well-known authors of noir procedurals – Faceless Killers by Sweden’s Henning Mankell and Rounding the Mark by Italy’s Andrea Camilleri – and explores how their two very different police detectives, Kurt Wallander and Salvo Montalbano, treat the fears, anxieties and insecurities aroused by immigration in their respective societies and, in doing so, assume in their own way the role of critical security analyst.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".