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Record W7037787781

En jämförande analys av prostitutionspolitik och omfattningen av människohandel i Sverige, Kanada, Tyskland och Nederländerna

2025· article· en· W7037787781 on OpenAlexaboutno aff

Bibliographic record

VenueDiVA at Umeå University (Umeå University) · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsnot available
Fundersnot available
KeywordsLegalizationLegislatureCriminalizationHuman traffickingIntervention (counseling)Affect (linguistics)Control (management)
DOInot available

Abstract

fetched live from OpenAlex

This study examines the effects of different legislative models regarding sex purchase through a comparative analysis of Sweden, the Netherlands, Germany, and Canada. Using quantitative methods, the study analyzes how different legislative models affect human trafficking for sexual purposes. The findings show that while no model has completely eliminated these issues, the Swedish model characterized by its criminalization of purchase while decriminalizing sale has been most successful in changing societal attitudes and reducing demand for sexual services. Legalization models in the Netherlands and Germany, where selling and buying sex is legal prove despite extensive regulations have encountered significant challenges with control and implementation. The study particularly notes the impact of digital development on sex trade as a growing challenge for all countries. The study also highlights the critical importance of implementing comprehensive strategies that combine robust legal frameworks with extensive social support systems and intervention programs.The research concludes by emphasizing the necessity for evolving approaches to combat human trafficking in an increasingly digital world, suggesting that successful policies must be flexible enough to respond to changing circumstances.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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