Bibliographic record
Abstract
Environmental factors can cause criminal impetus, motivation, realization and intention and, on the other hand, they may act as a barrier against crime realization. Right architecture designing and crime prevention initiatives in the environment are a necessary solution in crime prevention. Changes in spatial structure and environmental conditions would lead into changes in criminals’ behavioral patterns. The question of this article is to clarify the role of municipality in crime prevention at urban ambiences through spatial designing. By such assumption, municipality can play a very constructive role in reducing the crimes by relying upon crime prevention strategic principles through spatial designing. Therefore, by using the principles of crime prevention theories through spatial designing, one can prevent or reduce crime and delinquency occurrence in urban environment. Thus, citizens’ security will be promoted. On this basis, the origination of many criminal acts in marginal areas should be looked for in contradictory social and economic structures and their problems. In addition to play a vital role in spatial designing, Municipality would assist and provide vulnerable classes with its services such as identifying and retaining homeless people or begs. Cultural poverty, unemployment, low self – esteem, lack of infrastructural services and absconding are, inter alia, the factors which play a vital role in leading these people toward criminal acts.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".