MétaCan
Menu
Back to cohort
Record W2018282536 · doi:10.3138/cjccj.47.2.389

Recent Developments in Crime Prevention and Safety Policies in Finland

2005· article· en· W2018282536 on OpenAlexvenueno aff
Kauko Aromaa, Jukka-Pekka Takala

Bibliographic record

VenueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsCrime preventionSituational ethicsPolitical scienceGovernment (linguistics)Intervention (counseling)CriminologyCriminal justiceCultural criminologyPublic relationsPublic administrationSociologyLawPsychology

Abstract

fetched live from OpenAlex

Finland's National Council for Crime Prevention (NCCP) was established in 1989. A national crime prevention program was adopted by the government in 1999. The program follows the "Nordic model" and includes a national coordinating and steering body that provides funding and advice to local crime prevention committees and projects. The Nordic model of crime prevention involves a strong affiliation to areas outside the justice system and strikes a balance between social and situational crime prevention. A member since 2001 of the European Crime Prevention Network (EUCPN), the NCCP selects projects to be presented at the EUCPN's annual Good Practice Conferences as well as competing for the annual European Crime Prevention Awards. The overt politicization of crime policy issues may cause some rethinking. The Nordic model may need to be complemented by other approaches, such as early intervention, other methods of social crime prevention, and a growing emphasis on creating networks, cooperation, and partnerships with many different actors with a stake in preventing crime.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.008
Science and technology studies0.0040.004
Scholarly communication0.0090.003
Open science0.0020.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.145
GPT teacher head0.386
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations8
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénaleSame topicResearch in Social SciencesFrench-language works237,207