MétaCan
Menu
Back to cohort
Record W3015483606 · doi:10.1163/22116427_011010011

Denmark’s Obligations Regarding Mineral Resources in Greenland

2020· article· en· W3015483606 on OpenAlexaboutno aff
Bent Ole Gram Mortensen, Ulrike Barten

Bibliographic record

VenueThe Yearbook of Polar Law Online · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousNatural resourcePolitical scienceGovernment (linguistics)DeclarationIndigenous rightsPublic administrationPopulationLawHuman rightsEconomic growthEnvironmental protectionGeographySociologyEcologyEconomics

Abstract

fetched live from OpenAlex

Greenland has rich deposits of natural resources. Some of them could have the potential to be commercially developed. The exploitation of these resources could provide enormous opportunities for Greenland’s economic development. Greenland is part of the Kingdom of Denmark and enjoys far reaching rights of self-government. The population of Greenland is overwhelmingly Inuit, a people elsewhere recognized as an Indigenous people. The question concerning the exploitation of the natural resources is thus a complicated legal issue. International law provides indigenous peoples with special rights concerning the natural resources in their territory as referenced in the UN Declaration on the Rights of Indigenous Peoples and ILO Convention 169. The Kingdom of Denmark thus has international obligations regarding free, prior and informed consent. At the national level, the Self-Government Act includes provisions concerning natural resources, and this area is under the sole competence of the self-government. The Greenlandic Mineral Resources Act includes provisions on participation and consultation processes of local inhabitants. This article discusses whether the Kingdom of Denmark, through the Self-Government Act, lives up to its obligations under international law regarding the rights of the Inuit people in relation to the natural resources in their territory.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.310
Teacher spread0.272 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Yearbook of Polar Law OnlineSame topicArctic and Russian Policy StudiesFrench-language works237,207