MétaCan
Menu
Back to cohort
Record W4235319025 · doi:10.1093/yiel/yvw025

2. Canada

2015· article· en· W4235319025 on OpenAlexaboutno aff
Alexander Smith

Bibliographic record

VenueYearbook of International Environmental Law · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPublic administrationPolitical scienceClimate changeGovernment (linguistics)Kyoto ProtocolConference of the partiesConventionGreenhouse gasGeneral electionAction planLawPoliticsEconomicsManagement

Abstract

fetched live from OpenAlex

In May, the federal Conservative government unveiled a new goal for Canada that called for greenhouse gas (GHG) emissions to drop 30 percent below 2005 levels by the year 2030. In a general election, held on 19 October, the Liberal Party of Canada won a majority of the seats in Canada’s House of Commons and, shortly thereafter, formed a new government. The Conservative government had withdrawn Canada from the Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC) in December 2011, and in the 2015 general election, the Liberals ran on a platform to ‘provide national leadership and join with the provinces and territories to take action on climate change, put a price on carbon, and reduce carbon pollution.’ The Liberals also promised to ‘end the cycle of federal parties—of all stripes—setting arbitrary targets without a real federal/provincial/territorial plan in place’ and undertook to meet with provincial and territorial leaders at a first ministers conference within ninety days of the twenty-first Conference of the Parties to the UNFCCC in Paris to ‘establish a pan-Canadian framework for combatting climate change’ with ‘national emissions-reduction targets’ (Real Change: A New Plan for a Strong Middle Class, 39–40). The promised first ministers’ conference took place in March 2016, and a further meeting was scheduled for October 2016.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.236
Teacher spread0.223 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueYearbook of International Environmental LawSame topicPolitical Systems and GovernanceFrench-language works237,207