Islands and the rise of correlational epistemology in the Anthropocene: Rethinking the trope of the ‘canary in the coalmine’
Bibliographic record
Abstract
Once on the periphery of international debate, today small islands are seen by many as key to unlocking new ways of thinking about climate change and developing new practices of adaptation in the epoch of the Anthropocene. These approaches differ starkly from modernist, linear, causal frameworks that construct islands as vulnerable objects that require ‘saving’ or ‘protecting’. Instead, islands become instruments of productive knowledge, laboratories for investigation and learning, fundamental to an alternative, correlational, epistemology. In analysing these approaches, we take the prolific trope of islands as the ‘canaries in the coalmine’ in order to draw out the ontological implications of instrumentalising islands as ‘correlational machines’ in the Anthropocene. We raise fundamental problems with this literal instrumentalisation of islands and islanders, drawing out how these logics reduce island life to merely sensing and attuning to the co-relational entanglements of the Anthropocene, rather than offering higher normative aspirations for political change.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.072 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 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".