MétaCan
Menu
Back to cohort

A Tale of Two Taxes: The Fate of Environmental Tax Reform in Canada

2012· article· en· W1897310778 on OpenAlexaffabout
Kathryn Harrison

Bibliographic record

VenueReview of Policy Research · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnvironmental taxTax reformEconomicsPublic economicsPolitical science

Abstract

fetched live from OpenAlex

Abstract Policy makers who embrace market‐based approaches to environmental regulation, typically eschew carbon taxes in favor of the political advantages of cap and trade, which offers lower visibility of costs to consumers and the opportunity to allocate valuable permits freely to industry. Against this backdrop, the article examines two surprising proposals for carbon taxes, by the government of British Columbia (BC) and by the federal Liberal Party of Canada. Both reflected a triumph of party leaders' normative “good policy” motives over “good politics.” The BC tax alone succeeded first because it was adopted by a party already in government. Second, the onset of a recession before the next elections shifted voters' attention to the economy, which advantaged the BC Liberals but disadvantaged their federal counterparts. However, proposals for carbon taxes were unpopular in both jurisdictions, offering a cautionary tale concerning the fate of politicians' normative commitments absent electoral backing.

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)0.007
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.241
Threshold uncertainty score0.880

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0100.007
Scholarly communication0.0100.003
Open science0.0020.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.190
GPT teacher head0.375
Teacher spread0.185 · 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
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

Citations195
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueReview of Policy ResearchSame topicClimate Change Policy and EconomicsFrench-language works237,207