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Record W2909911278 · doi:10.4324/9781351019101-16

How can extractive industry help rather than hurt Arctic communities?

2018· book-chapter· en· W2909911278 on OpenAlexaboutno aff
Chris Southcott, Frances Abele, David Natcher, Brenda Parlee

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticBusinessThe arcticOceanographyGeology

Abstract

fetched live from OpenAlex

Across Northern Canada, as well as places such as Alaska and Greenland, the political empowerment of Indigenous governments has provided greater control over the conditions of resource development. In Northern Canada the signing of comprehensive land claim agreements, beginning in the 1970s, ushered in an era of political change that enabled Indigenous governments to regain self-determination over the development process. Land claim agreements created very specific kinds of power-sharing arrangements such as land use planning, co-management of wildlife as well as requirements for participation in environmental assessment and water/land regulation. The Environmental Impact Assessment (EIA) process had been recognized as an important means by which negative impacts can be considered and mitigated and by which communities can be better engaged. The short-term economic gains achieved through development projects can lead to unanticipated outcomes – greater dependency, uncertainty and conflict between social and ecological dynamics – that will diminish the likelihood of achieving community sustainability.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0080.006
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0140.004

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 head0.072
GPT teacher head0.314
Teacher spread0.242 · 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
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
Published2018
Admission routes1
Has abstractyes

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