MétaCan
Menu
Back to cohort
Record W2944419512 · doi:10.3390/soc9020035

Contrasting the Emergence of the Victims’ Movements in the United States and England and Wales

2019· article· en· W2944419512 on OpenAlexafffund
Marie Manikis

Bibliographic record

VenueSocieties · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDistancingContext (archaeology)CriminologyPolitical scienceLawSociologyGeographyCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

Over the years, the role of victims in the criminal process has considerably evolved in common law jurisdictions, particularly in the United States and England and Wales. These notable developments have varied greatly between these two jurisdictions. These differences are in great part attributed to the different forces and rationales behind the emergence of the early victims’ movements in these respective jurisdictions. Indeed, the movements in the United States and England and Wales adopted different philosophies, strategies, and members came from different backgrounds, which can account for the differences in policies. This article engages in a process of comparative distancing between the forces that drove the movements, as well as the context under which they operated in order to understand the different policies, legal responses and debates that relate to the role of victims of crime in the two selected jurisdictions.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0070.009
Scholarly communication0.0050.003
Open science0.0010.008
Research integrity0.0020.002
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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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 designQualitative
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

Citations10
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueSocietiesSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207