Exercising Agency in an International Socioscientific Controversy: The Use of Human and Material Agents to Assert Canada’s Sovereignty in the Arctic
Bibliographic record
Abstract
What determines a nation’s sovereignty over a particular territory? This question is now the subject of a heated debate on the international political scene, with global warming having rendered previously unreachable Arctic resources accessible to the five countries that have territorial claims in the far North: Canada, the United States, Russia, Denmark, and Norway. By building on the concepts of human and material agency, I demonstrate how both human and material agents represent the collective of Canada and thus give the Canadian government a material presence in the Arctic. This presence is key to actors such as the Canadian prime minister who are making the case for Canadian sovereignty in the Arctic region. This article therefore shows that the agency of participants in deliberation over socioscientific issues is largely influenced by the action of both other humans and material entities.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.068 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".