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Record W7052402135

Rasionalitas Pemerintahan Donald Trump Dalam Menyetujui Proyek Pipa Minyak Keystone XL Dengan Kanada

2021· dissertation· en· W7052402135 on OpenAlexaboutno aff

Bibliographic record

VenueUMM Institutional Repository (University of Maine at Machias) · 2021
Typedissertation
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAdministration (probate law)Pipeline (software)Keystone speciesGovernment (linguistics)State (computer science)Rationality
DOInot available

Abstract

fetched live from OpenAlex

The construction of an oil pipeline is a problem that has been controversial for the U.S government for recent years, moreover the Keystone XL pipeline construction with Canada. All the time, the Keystone XL project had been forcing with an environmental issues debate, because of that reason had made an impact on the determination of U.S foreign policy. Eventually, The U.S on the Donald Trump administration has been deciding to approve the Keystone XL pipeline construction with Canada after they were taking on more reviews.
\nThis research looks at how the Rationality Donald Trump Administration to Approve Keystone XL Pipeline Project with Canada, using the theory of Rational Actor Model for looking at how policy-making processes Donald Trump administration to approving the Keystone XL pipeline construction with Canada.
\nThe results of this research show that, The Donald Trump administration was approving the Keystone XL project because The U.S has national interests. The U.S state department stated that the U.S administration under Donald Trump would have signed to developing that pipeline because the project had congealed with U.S national interests, especially in the Interest of security and energy independence, and The U.S economic interests, like to increase of new jobs. Besides, The Donald Trump administration also considering the project as an alternative source for U.S energy due to The U.S problems with the other suppliers of energy, like Venezuela and Nigeria.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.179
Teacher spread0.174 · 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
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
Published2021
Admission routes1
Has abstractyes

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