Detecting Judgment Inconsistencies to Encourage Model Iteration in Interactive i* Analysis
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. Model analysis procedures which prompt stakeholder interaction and continuous model improvement are especially useful in Early RE elicitation. Previous work has introduced qualitative, interactive forward and backward analysis procedures for i * models. Studies with experienced modelers in complex domains have shown that this type of analysis prompts beneficial iterative revisions on the models. However, studies of novice modelers applying this type of analysis do not show a difference between semi-automatic analysis and ad-hoc analysis (not following any systematic procedure). In this work, we encode knowledge of the modeling syntax (modeling expertise) in the analysis procedure by performing consistency checks using the interactive judgments provided by users. We believe such checks will encourage beneficial model iteration as part of interactive analysis for both experienced and novice i * modelers.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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