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
The majority of labor transactions throughout much of history and a significant fraction of such transactions in many developing countries today are "coercive", in the sense that force or the threat of force plays a central role in convincing workers to accept employment or its terms. We propose a tractable principal-agent model of coercion, based on the idea that coercive activities by employers, or "guns", affect the participation constraint of workers. We show that coercion and effort are complements, so that coercion increases effort. Nevertheless, coercion is always "inefficient", in the sense of reducing utilitarian social welfare. Better outside options for workers reduce coercion, because of the complementarity between coercion and effort: workers with better outside option exert lower effort in equilibrium and thus are coerced less. Greater demand for labor increases coercion because it increases equilibrium effort. We investigate the interaction between outside options, market prices, and other economic variables by embedding the (coercive) principal-agent relationship in a general equilibrium setup, and study when and how labor scarcity encourages coercion. We show that general (market) equilibrium interactions working through prices lead to a positive relationship between labor scarcity and coercion along the lines of ideas suggested by Domar, while those working through outside options lead to a negative relationship similar to ideas advanced in neo-Malthusian historical analyses of the decline of feudalism. A third effect, which is present when investment in guns must be made before the realization of contracting opportunities, also leads to a negative relationship between labor scarcity and coercion. Our model also predicts that coercion is more viable in industries that do not require relationship-specific investment by workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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