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. Goal models are theories that describe how various stakeholder goals relate to each other. The constructs that such models use to represent these re-lationships focus on characterizing the nature of causality that connects goals, without, however, including temporal aspects such as the order in which goal sat-isfaction takes place. Nevertheless, introducing constructs to allow explicit repre-sentation of this ordering aspect has been shown to be useful for a variety of appli-cations. Furthermore, representation of such information need not necessarily be done through formalization or use of external representations; it is also possible through simple annotations on the core goal model. This allows for represent-ing the temporal dimension of goal models in a lightweight and concise manner. However, it does not come without influencing the established way to perceive goal models. In this paper, we discuss our experience in augmenting goal models with temporal information about goal satisfaction, which we performed for the purpose of representing and reasoning about behavioral variability.
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 | 0.001 |
| 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 | 0.000 |
| 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