Graph Model Under Unknown and Fuzzy Preferences
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
A new hybrid preference framework of the graph model for conflict resolution (GMCR) is proposed which allows decision makers (DMs) having both unknown preference and fuzzy preference to be taken into account in conflict modeling and analysis. The novel hybrid preference structure provides DMs with a more flexible technique to express preference. It is capable of covering the unknown preference of one feasible state over another, as well as fuzzy preference. Moreover, within the new hybrid preference structure, four extension forms of unknown preference are defined for different fuzzy stability definitions. These stability definitions under the new hybrid preference can be employed to thoroughly investigate complex conflicts existing in practical applications, and can offer enhanced strategic insights regarding the conflicts. A specific real-world water diversion conflict occurring in China, which includes multiple DMs and hybrid preference, is utilized to investigate how the new hybrid preference framework of the GMCR can be conveniently applied in practice.
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 | 0.001 |
| 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 | 0.001 |
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 it