Task Discretion, Labor-market Frictions, and Entrepreneurship
Bibliographic record
Abstract
Abstract An agent can perform a job in several ways, which we call tasks. Choosing agents’ tasks is the prerogative of management within firms, and of agents themselves if they are entrepreneurs. While agents’ comparative advantage at different tasks is unknown, it can be learned by observing their performance. However, tasks that generate more information could lead to lower short-term profits. Hence, firms will allocate workers to more informative tasks only if agents cannot easily move to other firms. When, instead, workers can easily move to other firms, agents may prefer to become entrepreneurs and acquire task discretion, even if their short-term payoff is lower than employees. Our model generates novel predictions with respect to, for example, how the wage dynamics of agents who switch between entrepreneurship and employment are affected by labor and contracting frictions. (JEL D83, J24, J62, J63, L26, M13).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".