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Record W4380590936 · doi:10.4337/9781800883789.00018

The Arctic: last frontier for energy and mineral exploitation?

2023· book-chapter· en· W4380590936 on OpenAlexaboutno aff
Ragnhild Freng Dale, Lena Gross

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFrontierIndigenousArcticAdventureGeographyEconomyPolitical scienceNatural resource economicsEthnologyHistoryArchaeologyEconomicsEcologyOceanographyGeology

Abstract

fetched live from OpenAlex

Tightly connected to ideas of manhood, adventure, and survival of the fittest, the Arctic has historically been imagined as the last frontier to conquer. In the last decades, the Arctic has caught new interest as a resource frontier for energy and minerals. Imaginaries of undiscovered reserves of hydrocarbons and minerals to satisfy increased global demand and consumption of electronic gadgets and electric vehicles, and renewable or ‘green’ energy such as wind and hydropower compete with, and simultaneously complement, romantic notions of the Arctic as a place of untouched nature and vanishing yet still preserved traditional indigenous lifestyles. Consequently, the Arctic is imagined as an unexplored, empty, and undeveloped frontier. In this imagined frontier space, indigenous peoples become one with nature, doomed to disappear and a hindrance for modern society. Multinational companies can therefore take what they desire, often with the blessing of the nation-state on which territory the resources are located. Indigenous peoples who have occupied these lands since before the existence of these nation-states are yet again exoticized, displaced, or see their land appropriated for industrial purposes. This chapter focuses on the impacts of such expansions and expropriations in two different parts of the indigenous Arctic: Sápmi and Northern Canada.

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 categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.377
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 studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.278
Teacher spread0.240 · 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 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

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