Avoiding Critical Members in a Team by Redundant Assignment
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
It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments-classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues.
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 | 0.001 | 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