Incentives for Quality Through Endogenous Routing
Why this work is in the frame
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Bibliographic record
Abstract
We study how rework routing together with wage and piece-rate compensation can strengthen incentives for quality. Traditionally, rework is assigned back to the agent who generates the defect (in a self-routing scheme) or to another agent dedicated to rework (in a dedicated routing scheme). In contrast, a novel cross-routing scheme allocates rework to a parallel agent performing both new jobs and rework. The agent who passes quality inspection or completes rework receives the piece rate paid per job. We compare the incentives of these rework-allocation schemes in a principal-agent model with embedded quality control and routing in a multiclass queueing network. We show that conventional self-routing of rework cannot induce first-best effort. Dedicated routing and cross-routing, however, strengthen incentives for quality by imposing an implicit punishment for quality failure. In addition, cross-routing leads to workload-allocation externalities and a prisoner's dilemma, thereby creating the greatest incentives for quality. Firm profitability depends on demand levels, revenues, and quality costs. When the number of agents increases, the incentive effect of cross-routing reduces monotonically and approaches that of dedicated routing.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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