Extending Group Role Assignment With Cooperation and Conflict Factors via KD45 Logic
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
Group role assignment with cooperation and conflict factors (GRACCFs) is a creative social computing method for team establishment. It can maximize the new team’s performance through role assignment considering potential cooperation or conflict factors among agents. However, this method has two bottlenecks in practical applications. First, in the scenario of establishing a new team from several existing teams, collecting the pertinent cooperation or conflict information encounters challenges. Second, GRACCF merely takes the CCFs as a part of the objective function for team performance, but this will underestimate the CCFs’ impacts on the sustainable development of the team. This article tackles these issues by extending GRACCF from a new viewpoint. It first designs a KD45 logic algorithm based on the KD45 logic system, which can discover the implicit cognitive CCFs through logical inferences with closure calculations. Then, it proposes an original team evaluation method that can help decision-makers determine the weights of team performance and CCFs’ impacts based on their demands. Large-scale simulation experiments indicate that the proposed solution is practicable and robust. The proposed method provides a solid decision-making reference for administrators when establishing a sustainable team.
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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 | 0.000 |
| 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