Kryminologia kulturowa? Antropologia kultury jako przyczynek do rozważań nad problematyką zabójstwa w krajach anglosaskich
Bibliographic record
Abstract
The paper covers the issue of cultural and ethnological perspective in contemporary criminology. The author presents various theories that are currently developing worldwide, addressing the problem of the role of culture (in the anthropological sense of the word) and its influence on the aetiology of criminal behaviour, social reaction to crime, and creation of role-modelling in different societies. The presentation is focussed mostly on the cultural and criminological comparison of three countries the United States of America, Great Britain and Canada – and their homicide data. These countries, although seemingly similar in many ways when traditionally analysed in criminology, differ significantly in terms of murder – not only statistically, but also at a deeper and more complex historical level. The author describes these differences, arguing that the cultural approach to criminological issues is crucial in explaining violence. The last part of the paper focuses on case-study examples from all the three countries under discussion; this serves as an illustration to the author’s postulate of incorporating the ethnological studies and research into the system of criminology.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".