The Arctic: last frontier for energy and mineral exploitation?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".