The Price of Un/Freedom: Indonesia's Colonial and Contemporary Plantation Labor Regimes
Bibliographic record
Abstract
Abstract Although often associated with colonial times, tropical plantations growing industrial crops such as rubber, sugar, and oil palm are once again expanding. They employ hundreds of thousands of workers, who still use remarkably basic tools. Flagging colonial continuities, labor activists campaign against the reemergence of unfree labor and “modern forms of slavery.” Paradoxically, labor activists also highlight the opposite problem: the casualization of plantation work, as workers are hired daily and fired at will. Recognizing that both “free” and unfree labor regimes have a long history in Indonesia, and plantations have pivoted between these modes more than once, my study compares plantation labor regimes in the colonial, New Order, and “reform” periods (post-1998) to answer three questions. First, given that employers always want to access disciplined labor at the lowest possible price, what were the conditions that led employers to rely on unfree labor in some cases, and “free” labor in others? Second, to what extent was unfreedom imposed as a response to excessive freedom among workers and peasants? Third, how were the costs of social reproduction distributed between workers and employers, and what pressures from workers or regulators (state, colonial, transnational) affected this distribution? In addition to published sources, I draw on my ethnographic research in West Kalimantan (2010–2015) to explore contemporary experiences of un/freedom among workers on state and private oil palm plantations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".