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Record W2318819581

Indigenous rights in national parks: The United States, Canada, and Australia compared

2013· article· en· W2318819581 on OpenAlexaboutno aff
Nicholas R Goldstein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousCustodiansIndigenous rightsPolitical scienceNational parkNatural resourceLawDeclarationRedressPublic administrationGeographyHuman rightsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

National Park systems across the globe enjoy broad public support as custodians of many awesome specimens of nature's majesty and protectors of our most popular public pleasure grounds. However, the traditional inhabitants of these lands are seldom enamored with the concept. This is because the preservation of these places for the enjoyment of all has conventionally entailed the outright expulsion of an original few. In many cases native people who cherished deep spiritual connections to these lands for generations were suddenly prohibited from utilising traditional homesteads, hunting grounds, and sacred ceremonial sites. Gradually, the dominant common law governments of many nations have come to realise that restoring customary resource use regimes, management rights, and even outright traditional ownership of these lands is not only just but can help advance their common interests. This paper utilises a comparative law approach to assess indigenous natural resource use and land management rights in the national parks of the United States, Canada, and Australia. It concludes with an assessment of each nation's compliance with the United Nations Declaration on the Rights of Indigenous People, an agreement the three nations each initially rebuked but have since come to endorse in their administration of national parks.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.442

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.011
Science and technology studies0.0120.006
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.299
Teacher spread0.275 · 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 designQualitative
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

Citations2
Published2013
Admission routes1
Has abstractyes

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