Affective dimensions of urban crime areas : towards the psycho-geography of urban problem areas
Bibliographic record
Abstract
Abstract. Traditional studies of crime areas within cities by geographers focus on the spatial variations in the incidence of crime, as well as the social deprivation and social disorganization of these areas. Although these social content and behavioural features are often highly correlated with crime areas. it is argued that analytical studies of crime areas need to be extended to deal with the feelings and attitudes of people in these areas.Ten separate dimensions of the affective domain are hypothesized, each of which describes different feelings and attitudes that characterize crime areas. These can be called «terrains of distinctive affective characters», namely: social inadequacy; despair or limited goals; exclusion and discrimination; acceptance of decay and destruction; anxiety and fear: spontaneity of actions and emotions: indifference to others; low selfcontrol and restraint; approval of subversive or deviant values; and peer group allegiance in gangs. Confirmation of these dimensions must wait for empirical testing but they point the way to the systematic development of a psycho-geography of crime areas in which the dimensions can be linked to different theories of criminal behaviour.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".