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Record W4366774539 · doi:10.1108/oxan-db278559

Canada needs record subsidies to attract new plant

2023· article· en· W4366774539 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2023
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyJoint ventureCompetition (biology)NegotiationProduction (economics)Agricultural economicsBusinessNatural resource economicsScale (ratio)International tradeEconomicsGeographyPolitical scienceCommerceMarket economyEcologyCartography

Abstract

fetched live from OpenAlex

Significance Volkswagen (VW) announced last month that its first battery facility outside Europe would be built in Ontario. While Canada has strong potential in this sector, the scale of the subsidy it offered indicates the degree of competition in North America to win such plants. Impacts Canada now has six of the seven major global auto manufacturers active within the country, with most committed to EV production. Negotiations continue over subsidies for another battery plant in southern Ontario, this one an LG-Stellantis joint venture. Canada’s minerals strategy to support EV production will boost the mining sector as the share of GDP from fossil fuels falls.

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)0.002
metaresearch head score (Gemma)0.007
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: Other · Consensus signal: Other
Teacher disagreement score0.050
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0090.001
Scholarly communication0.0060.002
Open science0.0010.001
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0500.004

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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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
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
Published2023
Admission routes1
Has abstractyes

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