Transforming regulations into performance models in the context of reasoning for outcome-based compliance
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
Recently, interest in performance modeling of out-come-based regulations has grown in the regulatory community. In this context, performance modeling refers to the measuring of important business aspects in a coordinated manner and the use of these measurements for improved decision making. Goal modeling techniques have shown to be beneficial when expressing and analyzing performance models. Since most regulations are still written in natural language, support for the transformation of regulatory text into performance models is needed. This allows regulators and regulated parties to keep working with familiar natural language regulations and to use goal models indirectly while avoiding a potentially significant learning curve for goal-modeling techniques. In this paper, we present such a tool-supported transformation to goal models expressed with the User Requirements Notation that enables reasoning about outcome-based regulations via widely available evaluation mechanism for goal models. The transformation is implemented in the jUCM-Nav goal modeling tool and illustrated with an example from the banking domain.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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