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Record W2269011030

Fear of crime: an international perspective of community-based factors

2003· article· en· W2269011030 on OpenAlexaboutno aff
Jody L. Carrington, Jeffrey E. Pfeifer

Bibliographic record

VenueSwinburne Research Bank (Swinburne University of Technology) · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)CriminologyPolitical scienceSociologyComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

In the past 30 years, the fear of crime has come to be understood as a problem that can be independent of crime itself. Researchers exploring crime prevention and community safety have suggested that the success of any programme rests not only on the actual reduction of crime but also on public perceptions of crime. For example, results from opinion surveys conducted in several countries indicate that most people believe crime rates are rising, regardless of actual trends, which suggest a general decline in police-reported violent and non-violent crime over the previous five years. The fear of crime is now considered to be a concept that is complex and multidimensional in nature. At the risk of oversimplification, for the purposes of this paper, fear of crime will be defined as being comprised of two broad, interacting concepts, namely, individual factors and community-based factors. Traditionally, factors associated with the individual (e.g., age, gender, ethnic origin) have been the focus of researchers exploring the fear of crime. This conventional approach, adopted primarily by researchers conducting large-scale national crime surveys such as the British Crime Survey and the General Social Survey (GSS) by Statistics Canada, have lead to many well-known conclusions. For example, researchers across many countries have found that, regardless of the measure used, women report higher levels of concern about criminal victimisation than do men. The economically disadvantaged, previous victims of crime, and those with physical vulnerabilities have also been identified as those who are consistently most likely to express a fear of criminal danger. More recently, researchers have begun to explore contributions beyond individual characteristics in explaining crime-related fears. For example the importance of the 'lived environment' and satisfaction with one's community (i.e., community-based factors), have been noted as at least as important as individual factors in understanding the fear of crime. In comparison to the research attention and subsequent understanding of the relationship between individual characteristics and the fear of crime, less effort has been devoted to identifying key community-based factors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.130
GPT teacher head0.422
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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