A Vision Towards A Conceptual Basis for the Systematic Treatment of Uncertainty in Goal Modelling
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
Goal modelling is one the most important early activities in requirements engineering. Here, we describe a vision for a conceptual basis for the systematic identification and treatment of uncertainty in goal modelling. We aim to characterize the wide variety of uncertainty in goal modelling and to provide a theoretical framework for systematic uncertainty analysis. We thus adopt Walker's taxonomy which distinguishes among three dimensions of uncertainty: location, level, and nature. In addition, we propose to adapt Walker's uncertainty matrix as a heuristic tool to categorize various dimensions of uncertainty in goal modelling to serve as a conceptual framework for improving comprehension and communication of uncertainty between modellers and stakeholders and among modellers themselves. Understanding the various dimensions of uncertainty is a vital step towards the sufficient recognition and treatment of uncertainty in goal modelling activities. This in turn will help identify and prioritize critical uncertainties, which affect the goal modelling process in its entirety. We thus propose a long-term research agenda and urge community contributions in this research direction.
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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 | 0.000 | 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 | 0.000 | 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