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Record W7098487035

The Kyoto Protocol: Canada’s Risky Rush to Judgment.” Ross McKitrick and

2002· article· en· W7098487035 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsParliamentKyoto ProtocolPrime ministerGovernment (linguistics)Greenhouse gasMontreal ProtocolEconomic impact analysis
DOInot available

Abstract

fetched live from OpenAlex

In this issue... Canada's Prime Minister Jean Chretien has pledged to ask Parliament to ratify the Kyoto Protocol before the end of 2002. However, little is known about how the accord would be implemented or what it would cost. By some estimates, its economic impact would be similar to that of the 1988 Free Trade Agreement between the United States and Canada. As a result, the government's timetable is precipitous, at best; at worst, it could lead to serious economic damage. The Study in Brief The Kyoto Protocol mandates a set of country-specific reductions of emissions of "greenhouse " gases that absorb and re-emit infrared radiation. Canada has agreed to a target of six percent below 1990 levels by the end of the decade, which will require about a 30 percent absolute emissions cut. Canadian Prime Minister Jean Chretien recently pledged that his government will ask Parliament to ratify the Kyoto Protocol before the end of the year. In light of the sparse information about how Kyoto will be implemented and how much it will cost, this timetable is, at best, precipitous; at worst, it risks serious economic damage. The federal government released a Discussion Paper last April outlining four hypothetical options for achieving compliance. We discuss some of the economics behind the estimated policy impacts, and conclude, among other things, that the Discussion Paper does not provide an adequate basis for making an informed decision on Kyoto. Given the scale of the policy commitment and the potentially farreaching economic effects, without a more thorough understanding of the economic impacts a decision to ratify on the basis of what has been presented thus far would be precipitous.

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)0.009
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.306
Threshold uncertainty score0.616

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0190.009
Scholarly communication0.0150.010
Open science0.0050.004
Research integrity0.0270.027
Insufficient payload (model declined to judge)0.0150.006

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 head0.041
GPT teacher head0.199
Teacher spread0.158 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2002
Admission routes1
Has abstractyes

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