Near Sets in Assessing Conflict Dynamics within a Perceptual System Framework
Bibliographic record
Abstract
The problem considered in this chapter is how to assess different perceptions of changing socio-technical conflicts. Our approach to the solution to this problem of assessing conflict dynamics is to consider negotiation views within the context of perceptual information systems. Briefly, perceptual information systems (succinctly, perceptual systems) are real-valued, total, deterministic information systems. This particular form of an information system is a variant of the deterministic information system model introduced by Zdzislaw Pawlak during the early 1980s. This leads to a near set approach to evaluating perceptual granules derived from conflict situations considered in the context of perceptual systems. A perceptual granule is a set of perceptual objects originating from observations of objects in the physical world. Conflict situations typically result from different sets of viewpoints (perceptions) about issues under negotiation. Perceptual systems provide frameworks for representing and reasoning about different perceptions of socio-technical conflicts. Reasoning about conflict dynamics is made possible with nearness relations and tolerance perceptual near sets used to define a measure of nearness. Several approaches to the analysis of conflict situations are presented in this paper, namely, conflict graphs, approximation spaces and risk patterns. An illustrative example of a requirements scope negotiation for an automated lighting system is presented. The contribution of this chapter is a new way of representing and reasoning about conflicts in the context of requirements engineering with near set theory.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".