Kebijakan Kanada Memperkuat Militernya di Samudera Arktik (2005-2013)
Bibliographic record
Abstract
This study aims to analyze the reasons why Canada strengthen its military in the Arctic. In 2005, under the leadership of Canadian prime minister, Paul Martin, Canada issued International Policy Statement which emphasize the strengthening of Canadian military in the Arctic. Furthermore, since 2006, under the new leadership of Canadian prime minister, Stephen Harper, Canada has announced its policy to strengthen the Canadian military in the Arctic. This study uses the qualitative research methods with references taken from books, journals, papers and valid news from web. This study uses state-level analysis. The author uses neorealist perspective, and the theory of action-reaction model by Barry Buzan to analyze the question of the research. Since the end of the Cold War, Canadian military activity re-emerged in the Arctic region in 2002, indicated by military operation and the presence of Canadian warships. In 2001, Russia claimed a region in the Arctic, which cause the territorial dispute with Canada. Russia also released the maritime doctrine in 2001, that emphasize the strengthening of Russian naval power in the Arctic region. Under the leadership of Russian president Vladimir Putin, and also to Dmitry Medvedev, Russian has strengthened its military in the Arctic by strategic flights, balistic misille launching, military exercises, and with construction and modernization of Russian Northern Fleet. Russian’s policy to strengthen its military in the Arctic is a threat to Canada, so since 2005, Canadian Government has strengthened its military in the Arctic. Key Word: Arctic Ocean, Canadian military strengthening, territorial dispute, threat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".