Solving the Flexible Task Allocation Problem via Group Role Assignment in Crowdsourcing
Bibliographic record
Abstract
Flexible employment remains a critical topic for crowdsourcing platforms. Decision-makers always focus their attention on maximizing operational efficiency, yet rarely prioritize the work experience of crowdsourcing employees. However, suboptimal working conditions contribute to user attrition, ultimately diminishing platform profitability. While the importance of worker experience in crowdsourcing platforms is recognized, the Flexible Task Allocation Problem (FTAP) has seen relatively little computational investigation due to the absence of powerful quantitative analytical tools. A notable exception is the Environment-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model, a mature computational framework with demonstrated efficacy in resolving similar socio-technical challenges. Consequently, this study builds upon the E-CARGO model and its Group Role Assignment (GRA) sub-model to provide a formalization and systematic analysis of the FTAP. By incorporating adjustments for distance and role-switching, the enhanced GRA model can significantly optimize the work experience of crowdsourcing employees, albeit at a slight cost to overall performance. This improvement helps crowdsourcing platforms retain more users and expand their scale. Relevant simulation experiments are conducted in this study to rigorously evaluate the effectiveness of these optimizations.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".