On coerced labor : work and compulsion after chattel slavery
Bibliographic record
Abstract
Acknowledgments ... vii List of Maps, Tables and Figures ... viii Notes on Contributors ... ix 1 Introduction ... 1 Marcel van der Linden and Magaly Rodriguez Garcia Part 1 Coerced in International and National Law 2 On the Legal Boundaries of Coerced ... 11 Magaly Rodriguez Garcia 3 Slavery: The Legal Tug-of-war between Globalization and Fragmentation ... 30 Nicole Siller 4 Forced and Institutional Change in Contemporary India ... 50 Christine Molfenter Part 2 Convict and Military 5 Forced in Colonial Penal Institutions across the Spanish, u.s., British, French Atlantic, 1860s-1920s ... 73 Kelvin Santiago-Valles 6 Convict in the Southern Borderlands of Latin America (ca. 1750s-1910s): Comparative Perspectives ... 98 Christian G. De Vito 7 `A military necessity which must be pressed': The u.s. Army and Forced Road in the Early American Colonial Philippines ... 127 Justin F. Jackson 8 Foreign Forced at Mitsubishi's Nagasaki and Hiroshima Shipyards: Big Business, Militarized Government, and the Absence of Shipbuilding Workers' Rights in World War II Japan ... 159 David Palmer Part 3 Agricultural and Industrial 9 Coerced Coffee Cultivation and Rural Agency: The Plantation-Economy of the Kivu (1918-1940) ... 187 Sven Van Melkebeke 10 As much in bondage as they was before: Unfree during the New Deal (1935-1952) ... 208 Nicola Pizzolato 11 State-Sanctioned Coercion and Agricultural Contract Labor: Jamaican and Mexican Workers in Canada and the United States, 1909-2014 ... 225 Luis F.B. Plascencia 12 Modern Slave Labor in Brazil at the Intersection of Production, Migration and Resistance Networks ... 267 Lisa Carstensen Part 4 In Lieu of a Conclusion 13 Dissecting Coerced ... 293 Marcel van der Linden Bibliography ... 323 Index ... 369
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".