A Novel Approach for Sequential Three-Way Decision Using Chi-Square Statistic as the Assessment Metric
Bibliographic record
Abstract
Three-way decisions play a crucial role in addressing decision-making problems in situations of uncertainty.They categorize the decision space into three discrete regions, specifically referred to as the Positive, Negative, and Boundary regions.These models are frequently employed in scenarios that involve the existence of multiple potential alternatives, necessitating the inclusion of a deferral option in addition to the two extremes.This methodology is especially advantageous in situations where the intricacy of the decision-making environment necessitates a more sophisticated examination of potential options that extend beyond a binary choice.The concept of information granularity is the foundation for the ability to conceptualize, comprehend, and apply a sequential three-way decision approach.When working with fine-grained granules, people have the freedom to carefully consider all of their options before deciding on any one.The application of sequential three-way decisions results from the availability of detailed information.Through the application of various techniques and a shift from coarse to fine-grained information granularity, this decision-making method establishes specific thresholds for efficient decision-making.This study introduces an innovative approach for exploring objects that have not made a definitive decision ultimately leading to the convergence of these objects into the Positive and Negative regions.The Chi-square statistic is employed as the evaluative measure for the process of dividing the decision space into three distinct regions.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".