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Record W3012189593 · doi:10.1080/23322705.2020.1738144

Access to Compensation for Trafficked Persons in Canada

2020· article· en· W3012189593 on OpenAlexaffabout
Sasha Baglay

Bibliographic record

VenueJournal of Human Trafficking · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsReceiptCompensation (psychology)RestitutionDamagesBusinessPolitical scienceLawPsychologyAccountingSocial psychology

Abstract

fetched live from OpenAlex

The article examines access to compensation for persons trafficked to and within Canada. It discusses the advantages and limitations as well as the interplay of four compensation mechanisms: restitution as a part of a criminal process, compensation from state victim funds, civil lawsuits for damages, and labor/human rights complaints. Neither of them has been specifically designed or subsequently modified to address the particular circumstances of trafficked persons, resulting in difficulties in access to and receipt of compensation by this group. Given the importance of compensation for achieving sustainable protection solutions for trafficked persons, a comprehensive study of the issue and strategic/policy action is necessary to improve access to compensation.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.072
GPT teacher head0.349
Teacher spread0.276 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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