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
Successful software must be both useful and innovative. Techniques for Requirements Engineering (RE) have mainly focused on utility, with a prominent body of work using goal modeling and analysis to ensure that systems meet user goals. However, these techniques are not designed to foster creativity, meaning that resulting systems may be functionally useful but not sufficiently innovative. Further work has focused on applying creativity techniques for RE through workshops. However, the free-form representation of creative workshop outputs (text and informal diagrams), although flexible, is not grounded in user goals, or able to take advantage of goal model analysis, e.g., trade-off analysis. Furthermore, successfully conducting a creative RE workshop requires much experience and soft-skills, as well as a significant economic commitment. In this work, we summarize initial progress aiming to combine goal modeling and creativity techniques for enhanced RE. We focus on methods and tools for introducing creative ideas to goal modeling, and grounding creative outputs in goal-oriented models. Our focus on tooling and methods help to alleviate the need for expert-lead, costly workshops. We outline and illustrate proposed methods.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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