Paying for freedom: Indentured labour and strategic default
Bibliographic record
Abstract
The focus of this paper is on labour arrangements characterized by indenture, bondage, or slavery, and the design of credit market policies to free these labourers. We examine bonded or indentured labourers that pay a fee to their patrons or employers for freedom from bondage in a multi-period game between the patron, labourers, and other lenders, where a labourer borrows the fee from a third party lender. Labourers are heterogeneous: they differ in their cost of committing strategic default in repaying the loan. The patron’s determination of the fee, and the possibility of strategic default by the labourer, are two key features that drive our results. Productivity gains are greatest when labourers with both high and low default costs pay the fee. However, there also exists a less socially optimal outcome where only the low default cost labourer participates. Importantly, we also develop a theory of the existence and persistence of bondage.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".