Quasi Group Role Assignment With Agent Satisfaction in Self-Service Spatiotemporal Crowdsourcing
Why this work is in the frame
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Bibliographic record
Abstract
Quasi group role assignment (QGRA) presents a novel social computing model designed to address the burgeoning domain of self-service spatiotemporal crowdsourcing (SSC), specifically for tackling the photographing to make money problem (PMMP). Nevertheless, the application of QGRA in practical scenarios encounters a significant bottleneck. QGRA provides optimal assignment strategies under conditions where both the number of crowdsourced tasks and workers remain stable. However, real-world crowdsourcing applications may necessitate the phased integration of new tasks. With the rapid increase in the number of tasks, a set of residual tasks inevitably exists that are difficult to complete. To maximize the completion of crowdsourced tasks, workers may be assigned low-yield or even unprofitable tasks. Given the reluctance of crowdsourcing workers to be overstretched for these tasks, along with the inherent characteristics of self-service crowdsourcing tasks, this can lead to the failure of the assignment scheme. To tackle the identified challenges, this article proposes the QGRA with agent satisfaction (QGRAAS) method. Initially, it sheds light on a creative satisfaction filtering algorithm (SFA), which is engineered to perform optimal task assignments while actively optimizing the profitability of crowdsourcing workers. This approach ensures the satisfaction of workers, thereby fostering their loyalty to the platform. Concurrently, in response to the phased changes in the crowdsourcing environment, this article incorporates the concept of bonus incentives. This aids decision-makers in achieving a tradeoff between the operational costs and task completion rates. The robustness and practicality of the proposed solutions are confirmed through simulation experiments.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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