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Record W3008032074 · doi:10.24043/isj.119

Islands and the rise of correlational epistemology in the Anthropocene: Rethinking the trope of the ‘canary in the coalmine’

2020· article· en· W3008032074 on OpenAlexvenueno aff
David Chandler, Jonathan Pugh

Bibliographic record

VenueIsland Studies Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropoceneTrope (literature)NormativeConstruct (python library)PoliticsEnvironmental ethicsAdaptation (eye)EpistemologySociologyHistoryPsychologyPolitical sciencePhilosophyLiteratureLawComputer scienceArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.316
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2020
Admission routes1
Has abstractyes

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