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Record W7062782079

Treaty Rights to Carbon Offsets within the Proposed Cap-and-Trade Regime in Ontario

2018· other· en· W7062782079 on OpenAlexafffundabout

Bibliographic record

VenueYork University Digital Library (York University) · 2018
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsYork University
FundersBC Cancer Agency
KeywordsTreatyNegotiationCarbon offsetSituatedArgument (complex analysis)Interpretation (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

The Government of Ontario has announced that it will join the Western Climate Initiative's cap-and-trade program, with the first compliance period starting as soon as January 1, 2017. The program will include the use of carbon offsets and establish an offset registry. This paper examines the question of whether Ontario's treaties with First Nations in Northern Ontario create a right to ownership and control of carbon offsets situated on traditional territories. First, I discuss the cap-and-trade regime as a whole, and the criteria for carbon offsets specifically. Then I explore some of the overarching obligations of the Crown in relation to aboriginal communities generally and the more specific rights of First Nations communities in Northern Ontario. Finally, I provide three arguments that First Nations could use to assert a right to a sui generis ownership of the carbon sequestration capabilities of their traditional territories. The first argument relies on an incidental right to the enumerated treaty rights, the second is framed as a right to harvest carbon offsets, and the third deals with the expansion of the interpretation of the treaties to include sharing in the benefits of the land. Though tenuous, these arguments provide some tools for First Nations to use in negotiations with the Crown during the development of offset protocols and regulations surrounding the offset market.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0280.000

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 head0.008
GPT teacher head0.162
Teacher spread0.154 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes3
Has abstractyes

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