Climate Change Totems and Discursive Hegemony Over the Arctic
Bibliographic record
Abstract
The Arctic and its animals figure prominently as icons of climate change in Western imaginaries. Persuasive storytelling centred on compelling animal icons, like the polar bear, is a powerful strategy to frame environmental challenges, mobilizing collective global efforts to resist environmental degradation and species endangerment. The power of the polar bear in Western climate imagery is in part derived from the perceived “environmental sacredness” of the animal that has gained a totem-like status. In dominant “global” discourses, this connotation often works to the detriment of Indigenous peoples, for whom animals signify complex socio-ecological relations and cultural histories. This Perspective article offers a reflexive analysis on the symbolic power of the polar bear totem and the discursive exclusion of Indigenous peoples, informed by attendance during 2015–2017 at annual global climate change negotiations and research during 2016–2018 in Canada’s Nunavut Territory. The polar bear’s totem-like status in Western imaginaries exposes three discursive tensions that infuse climate change perception, activism, representation and Indigenous citizenship. The first tension concerns the global climate crisis, and its perceived threat to ecologically significant or sacred species, contrasted with locally lived realities. The second tension concerns a perceived sacred Arctic that is global, pristine, fragile and “contemplated,” but simultaneously local, hazardous, sustaining and lived. The third tension concerns Indigenization, distorted under a global climate gaze that reimagines the role of Indigenous peoples. Current discursive hegemony over the Arctic serves to place Indigenous peoples in stasis and restricts the space for Arctic Indigenous engagement and voice.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.062 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".