Bibliographic record
Abstract
Singapore’s Arctic interests are typically explained by its limited regional market and the government’s stakes in shipping, maritime infrastructure, and global governance. Yet the city-state’s polar pursuits also reflect the government’s strategy of crafting a global national identity in step with its expansion of overseas economic activities. In this article, based on reviews of government speeches, documents, and press releases, observations at Arctic development conferences, and expert interviews, I first describe three regional shifts in the Arctic that have made Singapore’s involvement possible: the globalization of the Arctic economy, a transition from national government to global governance, and the production of the Arctic region as an investment frontier. Second, I elucidate the export-oriented industrial drivers of Singapore’s Arctic interests. These have led to the economy’s deterritorialization, which state discourses projecting Singapore as a “Global City” support. Third, I analyze how these two transformations—the Arctic’s globalization and Singapore’s deterritorialization—have together created an opportunity for the Singaporean government to “jump scale” in Arctic cooperation, specifically by shedding light on its partnerships with indigenous peoples’ organizations. As climate change accelerates, the Singaporean government’s Arctic efforts suggest that it sees the increasingly maritime region as a new scalar fix for overseas investment that it is securing through unconventional partnerships while living up to its quest to view the world as its hinterland. Singapore’s involvement in the Arctic may globalize the region’s economy, but it may also deepen northern dependence on place-based sectors like natural resources and shipping.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".