The tip of the iceberg: Ice as a non-human actor in the climate change debate
Bibliographic record
Abstract
The global climate change debate has the Arctic as a core region of concern and ice has become a central aspect of discourses. This article discusses ice representations from six different contexts linked to the 2009 United Nations Climate Change Conference (COP15) in Copenhagen. The author argues that even though the discussions often seem to be centred on ice alone, the latter enters into narratives and metaphors that have wider implications for how the Arctic and its Indigenous peoples are represented. Ice becomes a non-human actor, framing the discussions, acting in specific ways, and linking hybrid networks. Indeed it is used in diverse platforms by scientists, politicians, governments, and NGOs, as well as by Inuit hunters and fishers.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.052 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".