Rasionalitas Pemerintahan Donald Trump Dalam Menyetujui Proyek Pipa Minyak Keystone XL Dengan Kanada
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".