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Record W4225587956 · doi:10.26686/wgtn.17143196.v1

Sex trafficking, victimisation and agency: The experiences of migrant women in Malaysia

2020· dissertation· en· W4225587956 on OpenAlexfundno aff
Haezreena Begum Abdul Hamid

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsVictimisationContext (archaeology)CriminologySex traffickingAgency (philosophy)Punitive damagesPolitical scienceImmigrationEnforcementState (computer science)Law enforcementImmigration detentionForeign nationalSex workLawHuman traffickingPoison controlSociologySuicide preventionGeographyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Malaysia has criminalised sex work. However, its geographic location, porous borders and proximity to major trade and traffic routes have ensured a growth in sex trafficking activities. As a result, the ‘United Nations Office on Drugs and Crime’ and the ‘United States Trafficking in Persons Report’ have categorised Malaysia as a destination, transit and source point for sex trafficking in Asia. In response to such categorisations, Malaysia has ratified the (Palermo) ‘Protocol to Prevent, Suppress and Punish Trafficking in Persons Especially Women and Children’ and structured its anti-trafficking laws around prosecution, protection and prevention (referred to as the ‘3P’ policy). This thesis shows that the enforcement of victim-protection policies is carried out in contradictory ways in Malaysia. Trafficked women are portrayed as victims in need of care and protection, but also as individuals who have violated immigration laws and engaged in ‘immoral’ acts. This results in state practices that (re)victimise women through policing, immigration and court processes which are often deeply stressful, traumatising and violent. Punitive practices – including ‘state and rescue’ operations and long-term detention – have been legitimised and branded as ‘victim protection’. In this context, the thesis argues that current policies and practices represent a continuing form of violence against migrant women in Malaysia. Based on in-depth qualitative interviews, the thesis draws upon the stories of twenty-nine women who have been arrested and detained on the basis of their sex trafficked status as well as the perspectives of twelve anti-trafficking professionals involved in delivering the 3P policy. In doing so, the thesis shows how women are subject to prolonged victimisation at the hands of both traffickers and state authorities. However, it also provides an understanding of the ways in which ‘sex-trafficked’ women exercise courage, strength and resiliency in the face of the continuing harms against them. By demonstrating the nuances of agency throughout women’s migration experiences, the thesis challenges the stereotypical understanding of an ‘ideal’ victim of trafficking – commonly linked to images of passivity, weakness and worthiness. By providing an insight into women’s experiences of sex-trafficking and state ‘protection’, the thesis develops a more nuanced account of agency. Thus, the thesis argues that the state’s prevention of sex-trafficking as well as the protection of trafficked women cannot be progressively advanced without a fuller appreciation of women’s dual ‘victim’ and ‘agent’ identities. The thesis explores the implications of these findings on developing ‘anti-sex trafficking’ policies towards women in Malaysia.

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)0.006
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0140.012
Scholarly communication0.0060.005
Open science0.0020.009
Research integrity0.0030.005
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 head0.012
GPT teacher head0.284
Teacher spread0.271 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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