Identifying modern slavery in global supply chains: Leveraging monitoring technologies through multi-actor collaboration
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".