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Record W4213277462 · doi:10.33774/coe-2022-8jd2f

Green Strings: Principles and conditions for a green recovery from COVID-19 in Canada

2022· preprint· en· W4213277462 on OpenAlexaffabout
Vanessa Corkal, Philip Gass, Aaron Cosbey

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsInternational Institute for Sustainable Development
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Government (linguistics)Economic recovery2019-20 coronavirus outbreakPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political scienceGreen economyEnvironmental planningNatural resource economicsBusinessEconomic growthEconomicsGeographyLawMedicineSustainable developmentVirology

Abstract

fetched live from OpenAlex

The COVID-19 crisis, while difficult and tragic, also provides a critical opportunity to align efforts to meet Canada’s climate goals with the challenge of economic reconstruction post-pandemic. IISD has developed seven "green strings" recommendations: key principles, criteria, and conditionalities that should be applied to government measures for economic recovery from COVID-19 to ensure a green recovery. Canada’s leading environmental groups, representing close to two million people, have signed on to the recommendations.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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 head0.071
GPT teacher head0.325
Teacher spread0.254 · 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.

Study designObservational
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

Citations4
Published2022
Admission routes2
Has abstractyes

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