GRA With Secondment and Role-Importance-Based Training Plan
Bibliographic record
Abstract
Group role assignment (GRA) maximizes total benefits by assigning agents to appropriate roles, while GRA with a training plan (GRATP) further considers the impact of training. However, existing research on GRA and GRATP does not fully consider the demand for flexible adjustment of human resource assignment, which may lead to increased employment costs and project delays. Moreover, role importance significantly affects training resource assignment, as key roles contribute more to overall performance. Therefore, we propose the GRA with secondment and role-importance-based training plan (GRA-SRIT) model to address these issues. Specifically, this article introduces seconded personnel to temporarily replace the positions of agents undergoing training, ensuring the smooth continuation of the project. Depending on the role requirements, different training durations are assigned based on the specific requirements of their roles. In addition, trainers with different levels of expertise are assigned to agents based on role importance, ensuring that critical roles receive more specialized training, thus maximizing total benefit. Finally, experiments demonstrate the proposed model’s effectiveness in different scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".