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Record W3205453162 · doi:10.1177/00221856211054586

Modern slavery in global value chains: A global factory and governance perspective

2022· article· en· W3205453162 on OpenAlexaff
Donella Caspersz, Holly Cullen, Matthew C. Davis, Deepti Jog, Fiona McGaughey, Divya Singhal, Mark Sumner, Hinrich Voss

Bibliographic record

VenueJournal of Industrial Relations · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsMultinational corporationValue (mathematics)Factory (object-oriented programming)Global value chainAppropriationBusinessObligationGlobal governancePerspective (graphical)Corporate governanceEconomic systemEconomicsPolitical scienceInternational tradeLawComparative advantage

Abstract

fetched live from OpenAlex

‘Modern slavery’ describes various forms of severe relational labour exploitation. In the realm of global value chains and global factories that are led by multinational enterprises, modern slavery encompasses practices such as forced labour and debt bondage. Multinational enterprises organise and orchestrate global value chains into global factories that are highly adaptive to market pressures and changes in the external environment. We employ the global factory framework to conceptualise when and how global value chains become more vulnerable to modern slavery. We argue that combinations of the three global value chain characteristics: complexity, appropriation arrangements, and obligation cascadence, jointly form an environment in which modern slavery can evolve and take root. The degree to which forms of modern slavery become visible and recognisable depends on the particular combination of these characteristics. External factors can moderate the relationship between these factors (e.g. involvement of non-governmental organisations) or exaggerate their effect (e.g. a pandemic).

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

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

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 head0.035
GPT teacher head0.279
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations32
Published2022
Admission routes1
Has abstractyes

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