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Record W2976868543 · doi:10.14197/atr.201219137

The Quest to End Modern Slavery: Metaphors in corporate modern slavery statements

2019· article· en· W2976868543 on OpenAlexfundno aff
Ilse A. Ras, Christiana Gregoriou

Bibliographic record

VenueAnti-Trafficking Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
FundersMinistère de la Santé et des Services sociaux
KeywordsNewspaperAgency (philosophy)PublicationComplicityStatement (logic)Work (physics)Relation (database)BusinessLawPublic relationsPolitical scienceSociologySocial scienceEngineering

Abstract

fetched live from OpenAlex

This paper focuses on the modern slavery statements of three major UK high street retailers who are known for their relatively pro-active approach to the debate on corporate responsibility for ethical trading. Drawing on our earlier research in relation to metaphors in British newspaper reporting of modern slavery and human trafficking since 2000, we explore the metaphors that recur across the statements these companies have published in 2016, 2017 and 2018. These statements were published in accordance with the UK Modern Slavery Act 2015, which requires all commercial organisations operating in the UK, with a turnover greater than GBP 36 million, to publish an annual statement outlining the work done to assess and address (the risk of) modern slavery in their supply chains. We find that the metaphors used in these statements generally fail to acknowledge the agency of those workers affected by modern slavery and labour exploitation in a broader sense, the potential complicity of the retailers in sustaining an exploitative industry, and the underlying socio-economic factors that leave workers vulnerable to exploitation. We conclude that more needs to be done to account for the causes of modern slavery so that retailers can prevent rather than react to it.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.037
GPT teacher head0.348
Teacher spread0.311 · 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 designNot applicable
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

Citations16
Published2019
Admission routes1
Has abstractyes

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