Bibliographic record
Abstract
As a result of anthropogenic climate change, Inuit in the Arctic and island inhabitants in the Pacific Ocean both experience interrelated changes in their maritime environments. Global warming causes Arctic ice to melt, which leads to rising sea levels. As a result, local inhabitants in both regions experience the disappearance of their space (land and ice), paired with the arrival of new stakeholders with a diverse range of interests in the areas. As the inhabitants of the regions most vulnerable to the effects of climate change, Inuit and Pacific Islanders have engaged in counter-mapping and counter-narrating their space that colonial powers have previously conceptualized as isolated, remote, and peripheral. In contrast, the maps of Inuit Nunangat and the Blue Pacific illustrate and tell the stories of transnational spaces that have been collectively shared and used since time immemorial. These counter-mapping and counter-narrative approaches shape a new perception of the regions. This chapter contributes to conceptual development of environmental violence by discussing case studies of counter-mapping and counter-narration in the Arctic and the Pacific Ocean – as locals’ responses to experiences of structural and cultural violence to overcome their vulnerability, challenge power differentials, and satisfy their human needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".