Identifying modern slavery in global supply chains: Leveraging monitoring technologies through multi-actor collaboration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Monitoring technologies (e.g., worker voice technologies, remote sensors, satellite images) provide additional opportunities to improve the identification of modern slavery in supply chains. New collaborations among different actors are required to enable these technological capabilities. Yet little is understood about how collaboration between actors such as employers, certification and auditing bodies, non-governmental organizations (NGOs), and other vested intermediaries leverage monitoring technologies to identify modern slavery in global supply chains. Based on a qualitative inquiry of 32 interviews with leading actors in identifying modern slavery, we build upon two domains in the literature: the contracts domain and the conditions domain. Drawing on resource dependence theory (RDT), we show that valued resources (finance, access, skills, technology, and legitimacy) are held by various interdependent collaborators. We further show that identifying modern slavery can be enhanced through leveraging monitoring technologies embedded in collaborations, yet key contingencies (modern slavery posture, collaboration scope, cross-boundary interactions, and contextual embeddedness) facilitate (or inhibit) such an outcome. The paper offers an empirically grounded understanding of collaboration mechanisms to detect modern slavery in global supply chains.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it