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Las fuentes de energía renovable en Nova Scotia: Estrategias del gobierno provincial frente a las presiones del gobierno federal canadiense para alcanzar su meta de cero emisiones para 2050

2023· article· es· W4388623849 on OpenAlexaboutno aff
Oliver Santín, Gavin Fridell

Bibliographic record

VenueNorteamérica · 2023
Typearticle
Languagees
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoAustralian Government
KeywordsHumanitiesPolitical scienceNova scotiaGeographyArtArchaeology

Abstract

fetched live from OpenAlex

El gobierno federal canadiense bajo la administración del primer ministro liberal Justin Trudeau, estableció en 2021 su compromiso para que Canadá comenzara una reducción gradual de sus emisiones contaminantes hasta llegar a cero en 2050. Tal directriz, significa que las diez provincias y tres territorios del país, deben ajustar de forma autónoma sus estrategias para sumarse a esa meta en un ejercicio común establecido desde el gobierno central. Para el caso particular de la provincia atlántica de Nova Scotia, este mandato federal representa un enorme reto, ya que su infraestructura, desarrollada desde el siglo XVII, giró en buena medida alrededor de la generación de energía fósil. Este trabajo señala los obstáculos culturales y corporativos, las potencialidades de la energía alternativa, y las estrategias políticas que han debido desarrollar los líderes de la provincia para encontrar la estrategia más adecuada que sume a Nova Scotia a la dinámica que ya se ha emprendido en todas las regiones del país.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.306
Teacher spread0.267 · 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
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

Citations1
Published2023
Admission routes1
Has abstractyes

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