Bibliographic record
Abstract
This paper is an attempt to answer the question “could there be conflict – in particular, armed conflict – in the Arctic over disputed territory and claims of sovereignty?” In recent years, as climate change has thawed the ice in the northern regions, the prospect of new shipping lanes through once ice-locked corridors, as well as the prospect of access to new oil, gas, and mineral reserves, has led some scholars to believe that conflict could erupt as nations scramble to carve up one of Earth’s remaining ‘frontiers.’ While other scholars have debated the merits of these observations, few have undertaken a rigorous methodological approach that seeks to gauge the likelihood of conflict. This paper is thus an attempt to forge ground in making predictive analysis regarding this question. Using both historical qualitative analysis and statistical methods, I reach two conclusions: first, despite some scholars’ forbidding portrayals of the ineluctable coming strife over the Arctic, my research demonstrates that the likelihood of conflict is rather low. Cooperation, not conflict, is the most likely trend for Arctic diplomacy within the foreseeable future. And second, contrary to popular perceptions in the West, it is Canada, not Russia, who has demonstrated the highest relative likelihood of promoting conflict in the future among the nation-states evaluated.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".