Rationalizations and Institutional Deflection on the Ground Combatting Modern Slavery in Supply Chains
Bibliographic record
Abstract
Modern slavery has gained attention in society over the years alongside the enactment of legislation around the world targeting issues such as forced labour and highly exploitative forms of child labour in supply chains. However, there is a general convergence that global efforts to combat modern slavery have remained modest and largely ineffective. This chapter explores how businesses deflect growing institutional pressures surrounding the social responsibilities of businesses, particularly those concerned with taking action to combat modern slavery in supply chains. Drawing on insights from a range of businesses operating in Canada with global supply chains, prior to Canada’s enactment of legislation aimed at addressing the issue, the findings highlight an array of rationalizations that business professionals and businesses may embrace on the ground that give them and others the impression that they care about the issue of modern slavery in supply chains, but ultimately contribute to deflecting away the social responsibilities surrounding action that can contribute to combatting the issue. In exploring such rationalizations, this chapter advance our scholarly understanding of the important notion of deflection and its role in enabling modern slavery as well as our practical understanding of what deflection looks like on the ground.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".