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Legal regime of natural resource management in Canada

2020· article· en· W3046014230 on OpenAlexaboutno aff
Iaroslav Manin

Bibliographic record

VenueАдминистративное и муниципальное право · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNatural resourceResource (disambiguation)LegislationBusinessJurisdictionEnvironmental resource managementNatural resource economicsPolitical scienceEconomicsLawComputer science

Abstract

fetched live from OpenAlex

The subject of this research is the legal regime of natural resource management in the Canadian Kingdom as an example of one of the best sectoral practices of legal regulation of natural resource usage. Analysis is conducted on the normative legal acts that regulate rights to natural resource usage, delimitation of jurisdiction to “central” and “regional”, management in the area of natural resource. The object of this research is the natural resource usage relations in Canada. Special attention is given to the licensing of Canadian natural resource usage, determination of the types of licenses, and procedure of licensing. The author examines the relevant topics of taxation and fiscal stimulation of natural resource users, foreign investment, geological exploration, national and local legislation, right of indigenous peoples to natural resources, etc. The scientific novelty consists in demonstrating the current “picture” of legal regulation of natural resource usage in Canada. On the example of this kingdom, as the subject of right to ownership and use of resources, the author suggest considering an allotted plot of resources within its boundaries, while unallocated plots of resources should be counted as part of a single reserve of undistributed land (single object of law). The author proposes to conduct a mass geological survey of the Russian shelf in accordance with the Canadian model, implementation of the practice of attracting foreign investments, tax incentives, resource rent for the Russian citizens through legal construct of retirement savings, application of corporate restrictions following the example of Canada.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.981

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0120.005
Scholarly communication0.0070.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.018
GPT teacher head0.268
Teacher spread0.250 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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