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Scientific Facilities as a Subject Matter of “Infrastructure Law”: Une Approche Québéсoise

2021· article· en· W3204023992 on OpenAlexaboutno aff
А. О. Четвериков

Bibliographic record

VenueKutafin Law Review · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsLegislationCritical infrastructureSubject (documents)Public infrastructureCorporate governanceSubject matterLawBusinessPolitical sciencePublic administrationComputer scienceFinance

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.030
GPT teacher head0.250
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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