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
Role-based collaboration (RBC) theory is a promising paradigm for problem-solving in complex systems. Multigroup role assignment (MGRA) specifically tackles the task of assigning roles for multigroup collaboration. However, due to the constraint that an agent can only play a role in one group, the current MGRA models are incapable of handling when required agents outnumber the available supply. Group multirole assignment (GMRA) resolves the problem by permitting an agent to be assigned multiple roles, but it cannot address the assignment involving multiple environments-classes, agents, roles, groups, objects (E-CARGO) groups. Therefore, this article presents a comprehensive overview of the GMRA problem in multiple E-CARGO groups under various conditions, generalized as the multigroup multirole assignment (MGMRA) problem. The MGMRA problem primarily revolves around two key factors: the maximum number of roles that an agent can undertake within an E-CARGO group, and the maximum number of different roles across all E-CARGO groups, which have a significant impact on the sufficiency or necessity conditions of the algorithm as well as its performance. Therefore, a unified model and its special cases are proposed to solve the concrete assignment problems under different conditions. The effectiveness of models is verified through comprehensive experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 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 | 0.002 |
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