An Efficient Nonmarket Institution under Imperfect Markets: Labor Sharing for Tropical Forest Clearing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article examines the substitutability, productivity, efficiency, and evolution of an important agrarian nonmarket institution—labor sharing. Analysis of field‐level data on forest clearing through time among Amazonian shifting cultivators reveals that ( a ) family, hired, and cooperative labor are perfect substitutes, and hired and cooperative labor are equally productive, and both are more productive than family labor; ( b ) the combination of labor market and labor sharing makes productivity‐adjusted total labor use unconstrained by household and network endowments (i.e., efficient labor allocation); and ( c ) as labor composition is constrained by network endowments and liquidity, credit policies alter both labor composition and labor network formation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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