It is not just the economy: towards an alternative explanation of post-World War II crime trends in the Western world
Bibliographic record
Abstract
Arguably, the most popular criminological dictum of all times is the saying of French crimin - ologist Lacassagne, protagonist of the anti-Lombrosian French milieu school, that ‘each society has the criminality that it deserves’ (Lacassagne, 1843-1924, p. 364).1 In the early twentieth century this sociological theorem was elaborated in the thesis of Dutch criminologist Willem Bonger (1905). The main conclusion of his voluminous thesis, originally written in French in 1905 and later published in the USA and Canada in English versions in 1916, is that the level of crime in societies is determined by prevailing economic conditions.2 In Bonger’s Marxist view the endemic economic crises in capitalist societies engender under-classes wherein social pathologies such as alcoholism, prostitution and crime abound. According to this deterministic viewpoint crime in a society where the means of production have been nationalized, and the economy is planned by the state, would simply cease to exist.3 This optimist perspective seems to have exerted an enduring influence on theory formation in sociological criminology. Although few criminologists openly subscribe to Marxist notions of the causes of crime, criminological theories have throughout the twentieth century, been centred around concepts of class, conflict, relative deprivation, anomia, strain, inequality and unemployment (Merton, 1957; Vold, 1958). In communist countries Marxist criminology was de rigueur up to the 1990s. In the 1970s proponents of ‘critical criminology’ in the UK rediscovered Bonger and argued for a class-conscious criminology (Taylor, Walton and Young, 1973). Economists studying crime adopt similar positions. Many economists of crime tend to focus on the deterrent impact of criminal justice sanctions on criminal behaviour. If they analyse root causes, they join the criminological consensus by assuming that criminal activities are dependent on their opportunity cost, id est minimium wages and unemployment. From an economist perspective, criminal activities will increase in times of declining wages or increasing unemployment and vice versa (Grogger, 1998). The influential British social epidemiologist Richard Wilkinson has in a series of publications argued that inequality is not just a major source of stress-related health problems but also of violence and crime. The hypothesis of an overriding link between economic conditions and crime has a lasting grip on the political discourse on crime. In recent years several politicians and police chiefs in both the USA and Europe have warned for the likely crime boosting impact of the current economic crisis (UNODC, 2012).4
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".