Bibliographic record
Abstract
Introduction Over the past decade, the People’s Republic of China (PRC) has continued to rise as a great power on global politics and to challenge the interests of the US. While security competition is not new to US–China relations, its nature is changing and evolving in a new direction with past differences becoming more acute, and new areas are emerging, such as the Arctic region, that are intensifying this security dilemma (Allison 2017; Medeiros 2019; Mearsheimer 2021). The rapidly changing climate of the Arctic has given rise to China’s emergence as major influence within the region. Scholars have discussed the geopolitical ramifications of Arctic ice melt and accelerated prospects of an ice-free Arctic (Depledge 2021; Briggs 2013; Depledge 2021). As a rising global superpower, China has amassed incredible diplomatic, informational, military, and economic capabilities to influence and reshape the Arctic region (Briggs 2013; Connolly 2017). China was granted status as an observer to the Arctic Council in 2013, providing it a seat at the table with a new-found voice and ability to engage within the region. This engagement, primarily through economic and diplomatic means, has for many raised serious concerns regarding its long-term intentions considering its heavy-handed practices in other regions of the world (Miller 2019; Vitug 2018; Garlick 2019). China’s efforts within the region are considered a threat to long-term regional stability, impacting the human, environmental, and national security objectives for several members of the Arctic Council, including the US, Canada, and Sweden (Cassotta et al 2015; Lackenbauer et al 2018; Doshi et al 2021). With regional resource, access, and longer-term interests at stake, Chinese investment and diplomatic overtures within the Arctic serve as a point of regional friction prompting numerous countries to take notice and address the competitive challenges posed by this evolving rivalry (Connolly 2017; Depledge 2021). While there have been many studies on the influence of China in the Arctic region, there has been little research on how abrupt climate change in combination with Chinese actions in the region could create unpredictable black swan events that undermine US and regional security (Zysk and Titley 2015; Valentine et al 2021).
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".