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Record W7135940542

Comparison of Obama and Trump Arctic Policies and Their Impact on US-Canada Relations

2025· dissertation· cs· W7135940542 on OpenAlexaboutno aff
Matěj Petrů

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2025
Typedissertation
Languagecs
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsForeign policyContext (archaeology)Administration (probate law)ArcticInternational relationsThe arcticClimate policy
DOInot available

Abstract

fetched live from OpenAlex

This bachelor's thesis compares the Arctic strategies of the Barack Obama and Donald Trump administrations and their effects on the bilateral relationship between the United States and Canada. The thesis uses the idea of cooperative and coercive approaches in foreign policy to examine how the different strategies of the two administrations influenced cooperation in environmental protection, energy policy, and geopolitics in the Arctic. Through a comparative case study, it shows that the Obama administration focused on multilateral cooperation, climate policy, and working together with Canada, while the Trump administration favored a more unilateral approach, prioritized energy development without much concern for environmental impact, and emphasized American national interests in the Arctic. These differences affected the level and quality of cooperation between the two countries. The thesis helps to better understand how U.S. Arctic policy has changed and how it influences important transatlantic relations in the context of shifting global conditions and geopolitical interests.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.722

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0090.004
Scholarly communication0.0070.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.307
Teacher spread0.294 · 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
Published2025
Admission routes1
Has abstractyes

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