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Record W6958095816 · doi:10.60692/yz9g0-anj12

Privatizing Uncertainty and Socializing Risk: Indigenous Legal and Economic Leverage in the Federal Trans Mountain Buy-Out

2023· article· en· W6958095816 on OpenAlexaffabout

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOpposition (politics)IndigenousGovernment (linguistics)Human rightsCorporationHonourStipulationCommitHuman settlement

Abstract

fetched live from OpenAlex

[From Introduction]: "In an interview in early-September 2018 shortly after the federal buy-out of the Trans Mountain Pipeline Expansion Project (TMEP), Prime Minister Justin Trudeau proclaimed that this project would be dead if it were not for the higher risk tolerance of the federal government compared to that of previous owner, Kinder Morgan, Inc. (KM). In other words, the federal government could guarantee completion where a private corporation had failed to make the project viable – perhaps even because the project no longer needed to be economically viable to succeed. This statement of the sitting Prime Minister is troubling for many reasons, central among which because he implies that governments have a higher risk tolerance vis-à-vis abrogating Aboriginal title and rights, when indeed they have a constitutional obligation to maintain them and the honour of the Crown. Opposition to the pipeline, led by First Nations and environmental groups, was after all one of the reasons KM cited for selling off this asset. International environmental, Indigenous, and human rights obligations apply directly to governments, so if anything, the government of Canada should commit to implementing higher standards than industry."

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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