Conflict Analysis Based on Three-Way Decision Theoretic Fuzzy Rough Set over Two Universes
Bibliographic record
Abstract
Conflict is an inevitable feature of human endeavor.The conflict analysis has great theoretical and practical significance.Some scholars have discussed three-way decision in conflict analysis with probabilistic rough set (RS) over two universes, but have not probed deep into the fuzzy rough set (FRS) for conflict analysis.To make up for the gap, this paper firstly introduces the concepts and properties of conflict information system (CIS) and probabilistic CIS over two universes.Next, a brand-new system called FRS-CIS was established, followed by the construction of probability measure function and two novel mappings in the probabilistic FRS-CIS over two universes.Furthermore, the authors discussed the properties of probability measure and the lower and upper approximations of 𝑌 with parameters 𝛼 and 𝛽 corresponding to each mapping.Finally, (0,1)-probabilistic FRS and 0.5-probabilistic FRS were presented for the FRS-CIS and explained with examples, according to the three-way decision theoretic RS under the CIS over two universes.The research results shed new light to solving the FRS under FRS-CIS over two universes.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".