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Record W7010135097

Heating Up, Backing Down: Evaluating recent climate policy progress in Canada

2022· report· en· W7010135097 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasClimate policyClimate changeFossil fuelWork (physics)Public policyGlobal warmingEnergy policy
DOInot available

Abstract

fetched live from OpenAlex

This is a co-publication by the Canadian Centre for Policy Alternatives (CCPA) and the Adapting Canadian Work and Workplaces to Respond to Climate Change research program (ACW). It assesses the climate policy progress of Canadian governments over the past two years with respect to long-term greenhouse gas emission reductions and concludes that positive progress in British Columbia and Quebec over the past few years is outweighed by backsliding in other provinces. The new governments in Alberta and Ontario—Canada’s two biggest carbon polluters—have reversed the climate policies of previous governments, which puts Canada’s already-unlikely national targets even further at risk.
\nThe report identifies two growing threats to climate policy progress in Canada:
\n1. A narrow public debate over carbon pricing is eroding political will for a more comprehensive climate policy approach. There are many other policies that are less controversial and can be just as effective at reducing emissions.
\n2. Canadian governments have been unwilling to introduce supply-side energy policies designed to restrict the production of fossil fuels, even though keeping much of our oil and gas in the ground is necessary to avoid the worst effects of global climate breakdown.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.014
Science and technology studies0.0020.000
Scholarly communication0.0010.006
Open science0.0030.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.240
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

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
Published2022
Admission routes1
Has abstractyes

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