Innovations in the Business Models of Modern Slavery: The Dark Side of Business Model Innovation
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the scant attention paid to the dark side of business model innovation by empirically examining innovations in the business models of modern slavery. Our paper focuses on how the business models of slavery in advanced economies like the US and UK have evolved since the practice was legally abolished in the 19th Century. We find that while some continuities with the business models of traditional slavery exist, novel forms of business models have emerged based on new actors, activities, and linkages between activities. We categorize these innovations according to the actor involved (producer/intermediary) and how value is created and captured (revenue generation/cost reduction), giving rise to four innovative models. We discuss our findings considering the literature on business model innovation, the dark side of organizations, and the business of modern slavery, as well as outline implications for policy and practice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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