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Record W2885742462 · doi:10.4337/9781784712303.00009

Environmental and Indigenous issues associated with natural gas development in British Columbia

2018· book-chapter· en· W2885742462 on OpenAlexaboutno aff
Anna Vypovska, Laura A. Johnson, Dinara Millington, Allan Fogwill

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2018
Typebook-chapter
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousWildlifeEnvironmental planningThreatened speciesEnvironmental impact assessmentGreenhouse gasNatural resourceEnvironmental resource managementCumulative effectsGeographyEnvironmental protectionNatural (archaeology)Political scienceEnvironmental scienceHabitatEcologyLawArchaeology

Abstract

fetched live from OpenAlex

This chapter discusses key environmental and Indigenous peoples’ issues facing development of the natural gas and liquefied natural gas (LNG) industry in the Province of British Columbia, and examines the main approaches to mitigate, manage and monitor these issues effectively. The authors reviewed environmental assessment applications for 29 major natural gas and LNG projects in British Columbia that have undergone a typical environmental assessment process with the provincial or federal responsible authorities since 2010, as well as the content of primary regulatory documents and issues identified in relevant case law. The key environmental issues identified from the review include significant residual adverse effects related to greenhouse gas emissions; significant residual adverse effects and cumulative effects to rare and threatened wildlife species; and cumulative adverse impacts of natural gas development. The most common potential adverse impacts on Indigenous peoples’ interests summarized in the review include but are not limited to effects on health and socio-economic conditions; physical and cultural heritage; the current use of lands and resources for traditional purposes; sites of historical and archeological significance; and potential cumulative impacts on Aboriginal interests. The chapter also provides examples of key approaches to mitigate the foregoing issues and stresses the importance of effective consultation and engagement with Indigenous groups at early stages of the proposed projects development.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.102
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0070.003
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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