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Record W7055123239

Climate Change Inaction in Canada: Political Subsystems and Policy Outcomes in the Oil & Gas Industry, 1999-2019

2021· other· en· W7055123239 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2021
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsFossil fuelClimate changeGreenhouse gasGovernment (linguistics)Energy policyPoliticsPetroleum industryClimate policy
DOInot available

Abstract

fetched live from OpenAlex

Despite increasing urgency of the climate crisis, Canada is unlikely to meet its 2030 greenhouse gas emissions reduction target under the Paris Agreement. The expansion of the countrys fossil fuel industry is one of the main causes for Canadas emissions. Consequently, recent studies have adopted a policy network approach to outline the relationship between the federal government and the fossil fuel industry to explain the countrys inaction. However, the relationship between this network and actual policy outcomes remains unclear. Hence, this study determines the extent to which climate and energy policy change applied by the federal and Alberta provincial governments reflect the interests of the fossil fuel industry. The main findings point to the fossil fuel industry having had substantial political influence on climate and energy policy decisions over the last twenty years, although its influence has been increasingly contested over time. However, this network remains influential in Canadian politics.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.142
Threshold uncertainty score0.995

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0090.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.201
Teacher spread0.184 · 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 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
Published2021
Admission routes1
Has abstractyes

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