Bibliographic record
Abstract
We propose a method and a process for legal software requirements extraction and compliance checking. We describe a requirements extraction model, a set of rules for specifying the format of the extracted information, a set of UML-based principles for translating the extracted information into a language based on predicate logic, and finally, a tool that analyzes the resulting logic model and displays the results of the analysis. The translation principles are based on a Governance Analysis Model (GAM) which is described in UML; the language is our Governance Analysis Language (GAL) and the tool is our Governance Analysis Tool (GAT). MIT's logic analyzer Alloy is the engine on which GAT runs. GAL is translated into assertions in Alloy's language and the Alloy tool can find counterexamples indicating situations of non-compliance.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".