Bibliographic record
Abstract
This article examines how ill-defined legal norms around modern slavery are being outlined in supply chain legislation and then interpreted by management professionals. Building on an infrastructural analysis of supply chain governance, I uncover the set of practices that underlie recent regulations around modern slavery. I track the implementation of these laws by following the “chain of translation,” whereby information is transformed from on-the-ground raw data, to quantitative metrics of modern slavery risks, and, finally, to polished corporate statements. This analysis focuses on the critical role being played by the Supplier Ethical Data Exchange (Sedex), which is a platform for sharing responsible sourcing data. While Sedex is not an auditor and is not governed by lawyers, it is nonetheless serving an important function in interpreting legal norms around modern slavery and facilitating the implementation of supply chain laws. Yet there are potential costs to its expansive role. Sedex is translating modern slavery into a management problem largely based on quantitative metrics such as indicators and risk scorecards. While Sedex provides limited opportunities for public participation, it needs to be more transparent with respect to the methodology behind its metrics and provide further opportunities for comment by parties underrepresented in its governance.
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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.083 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".