Outsourced Products Task Allocation via Group Multirole Assignment with Considering Variance
Bibliographic record
Abstract
The phenomenon of uneven product quality in software outsourcing enterprises is common. Even for products with similar functionality, the company cannot guarantee the final delivery quality. Effectively solve the stability of product quality, which is more conducive to long-term cooperation between customers and the company. Hence, this paper formalizes the Outsourced Products Task Allocation (OPTA) problem via the Environments-Classes, Agents, Roles, Groups, Objects (E-CARGO) model. Through its sub model Group Multirole Assignment (GMRA), a team with the optimal qualification value can be obtained. However, the stability of the assignment result is still not guaranteed. Therefore, this paper innovatively introduces the concept of variance and proposes the Group Multiple Role Assignment with Considering Variance (GMRACV) model to tackle this issue. And make better improvements to it, achieving a performance loss of 1% in exchange for about 32% stability. Large-scale randomized experiments show that the proposed model can effectively reduce the variance between products while maintaining the overall quality of all products. And for data with different distributions, the model can still obtain excellent results stably, which further verifies the feasibility of the model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".