China’s New Arctic Strategem: A Strategic Buyer’s Approach to the Arctic
Bibliographic record
Abstract
Since China does not border on the Arctic, it has used alternative approaches to gain access to the region. This paper argues that China has gained a foothold and demonstrated its energy interests in the Arctic by using a strategic buyers approach. China’s SOEs have purchased, invested in, and participated in joint ventures with Arctic-related companies such as Northern Cross and Rosneft. Chinese academics and officials are aware that the Arctic is melting and realize the region’s potential wealth. They are also aware that tapping into the Arctic’s resources, especially through Russia, may soon become the most efficient method for securing China’s energy needs. China’s Arctic approach can be summarized as having consisted of two phases: the rhetoric and culmination of ideas phase and the strategic buyer phase. Regarding the first phase, Chinese scholarly writings, media reports, diplomatic rhetoric, and PLA comments provided a spotlight on, and created an awareness of, China’s interests in the Arctic. These groups fostered important Chinese ideas and debates on how China should approach Arctic issues. The second phase (which we are currently in) has shown that China has made Arctic purchases, investments, and joint ventures with Russia, Canada, and Iceland and also seems to be on the verge of obtaining something more concrete with Greenland in the not-too-distant future. This has made China’s Arctic strategy more apparent and helped differentiate noise from true courses of action. China’s current Arctic moves and approaches over the past several months have made the PRC’s Arctic strategy much less opaque and increasingly more visible and coherent. Normal 0 false false false EN-CA X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-CA;}
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".