Using i* to Elicit and Model Transparency in the Presence of Other Non-Functional Requirements: A Position Paper
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
Transparency has been, for long, a general requirement for democratic societies. The right to be informed as well as to have access to information has been an important issue on modern societies. Nowadays, Transparency has been elevated to a must have property to be delivered by governments and businesses. In an era when computer systems are ubiquitous and present in almost every aspect of our lives, it seems natural that Transparency becomes a key requirement in our software systems. We believe that Transparency can rarely be satisfied. The best we can do is to satisfy it within acceptable limits (satisfice). Therefore, we consid- er Transparency a Non-Functional Requirements (NFR) that should be elicited and modelled in the presence of other competing and synergistic NFR. Intentions be- hind the adoption of Transparency may play an important role while eliciting solu- tions for software to deliver appropriate levels of Transparency. Hence, we believe that the i* framework is ideal to elicit and model Transparency. This work will show initial ideas for using i* to support this effort.
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 | 0.000 | 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