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Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWater Resources and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousCorporate governanceWork (physics)State (computer science)Traditional knowledgeIndigenous rightsQuality (philosophy)

Abstract

fetched live from OpenAlex

Human relationships with the Great Lakes Basin Ecosystem were regulated by various treaties and governance systems for thousands of years prior to European colonization. During these millennia, the waters and surrounding lands of the region were maintained in a healthy state that provided a wealth of benefits to all human and non-human inhabitants. During the relatively short period since colonization, the ecosystem has suffered extensive degradation, with indigenous peoples suffering a disproportionate level of health and economic impacts as a result. Binational agreements such as the Great Lakes Water Quality Agreement between the United States and Canada have for decades been concerned with maintaining and restoring the water quality of the region, but have only recently come to recognize the importance of indigenous rights, responsibilities and knowledge with respect to the area. This recognition is welcome and long overdue, but continues to fall short of respecting an indigenous voice on a nation-to-nation basis. In order for indigenous perspectives to be meaningfully applied to the protection of Great Lakes waters, indigenous peoples must be afforded the opportunity to work collaboratively with other interests on Great Lakes protection initiatives from their inception, rather than trying repeatedly - and often ineffectively - to provide mere “input” into existing non-indigenous frameworks. A summary of indigenous concerns with respect to the Great Lakes environment is offered as a starting point for discussion around the development of a more meaningful collaboration.

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 score0.929
Threshold uncertainty score0.945

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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

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