Bibliographic record
Abstract
We report on a project consisting in the application of the Intertextual Semantics modeling method (IS; Marcoux 2006 , Marcoux & Rizkallah 2007a , Marcoux & Rizkallah 2009 ) to a particular type of legal document: the “Agreement as to the conduct of the proceedings,” used in the Province de Québec (Canada). This was done as a sub-project of the Towards Cyberjustice project in the Faculty of Law at Université de Montréal . One of the project objectives was to verify whether the availability of a semantic model of a document type (more precisely, a IS model) would impact on (and hopefully help) the development of an application for the collaborative authoring of such documents. We first explain how the project lead to many extensions to the then existing rudimentary IS platform ( Marcoux 2009 ), and describe the most important of them. We then present a few unforeseen difficulties that arose in the process of modeling, and the lessons learned. Although no definite answer was obtained as to whether IS can directly help in the development of applications, the project showed it can at least help indirectly, by forcing fundamental questions to be asked early on in the process. In our case, applying IS modeling revealed that nobody really knew from the outset what the target community was, nor what their actual needs were. This is a good illustration of the kind of effect IS can have on application development projects: making sure fundamental questions do not go unasked too long.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".