Scientific Facilities as a Subject Matter of “Infrastructure Law”: Une Approche Québéсoise
Bibliographic record
Abstract
The article deals with the original approach of Canadian French-speaking province (federal entity) to legal regulation of scientific facilities as a type of infrastructural objects governed by “infrastructure law.” The author firstly proves that the expression “scientific facility” and “Megascience” represent no more than the specific types of social infrastructure and, thus, generally denoted in legal instruments as “research infrastructure” which may be qualified as “large” (Megascience), “medium”, “small” etc. Further the article explores the modern legislation of Quebec which, unlike other countries, has decided to create a full-fledged “infrastructure law” governing, amongst other types of infrastructure, the research infrastructure. The article points out and analyses the particularities and principle findings of Quebec infrastructure laws and by-laws: the “supraministerial” governance of all infrastructure projects, the general public infrastructure company (Quebec Society of Infrastructures) etc. The latest developments in the Quebec “infrastructure law” relating to information infrastructures are also taken into account.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".