Reputation-aware task allocation for human trustees
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
Compared to automated entities, human trustees have two distinct characteristics: 1) they are resource constrained (with limited time and effort to serve requests), and 2) their utility is not linearly related to income. Existing research in reputation-aware task delegation did not consider these two issues together. This limits their effectiveness in human-agent collectives such as crowdsourcing systems. In this paper, we propose a distributed reputation-aware task allocation approach - RATA-NL - to address these issues simultaneously. It is designed to help an individual human trustee determine the optimal number of task requests to accept at each time step based on his situation to maximize his long term well-being. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of the trustees, and provides provable performance guarantees. RATA-NL has been compared with five state-of-the-art approaches through extensive simulations based on human task delegation behavior abstracted from a user study involving over 100 trustees for eight weeks. The results demonstrated significant advantages of RATA-NL, especially under high workload conditions.
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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 | 0.001 | 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