Qualitative vs. Quantitative Contribution Labels in Goal Models: Setting an Experimental Agenda.
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
Abstract. One of the most useful features of goal models of the i * family is their ability to represent and reason about satisfaction influence of one goal to another. This is done through contribution links, which represent how satisfaction or denial of the origin of the link constitutes evidence of satisfaction/denial of its destination. Typically in the i * family, the nature and level of contribution is represented through qualitative labels (“+”, “−”, “++ ” etc.), with the possibility of alternatively using numeric values, as per various proposals in the literature. Obviously, our intuition seems to suggest, labels are easier to comprehend and to come up with, while the use of numbers raises the question of where they come from and what they mean, adds unwarranted precision and overwhelms readers. But are such claims fair? Based on some early experimental results, we make the case for more empirical work on the matter in order to better clarify the differences and understand how to use contribution representations more effectively. Key words: requirements engineering, goal modeling, i-star 1
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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 | 0.001 | 0.001 |
| 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 | 0.003 |
| 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