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Record W3125025918 · doi:10.24043/isj.145

Embodying the Anthropocene: Embattled crustaceans, extractivism, and eco-tourism on Christmas Island (Indian Ocean)

2021· article· en· W3125025918 on OpenAlexvenueno aff
Philip Hayward

Bibliographic record

VenueIsland Studies Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropoceneTourismGeographyFutures contractHistoryEconomyArchaeologyEnvironmental ethicsBusiness

Abstract

fetched live from OpenAlex

Christmas Island, in the north-eastern Indian Ocean, remained uninhabited until 1888 when British entrepreneurs established a phosphate mining operation that has continued to the present. Over the last 132 years, the island has experienced a series of impacts that typify the effects of extractivism globally. Acquired by Australia in 1958, the island has also been the site of a major immigration detention centre, set up in 2006 to process and deter Asian asylum seekers. In recent decades, tourism has also been added to the economic mix in a form primarily orientated to the island’s distinct fauna, an enterprise that co-exists uneasily with established mining and internment operations. In these regards, the island has rapidly experienced a range of transnational pressures that have distorted and compromised its environment. As such, the island’s recent ‘biography’ exemplifies the impact and scale of integrated Anthropocene factors. Drawing on recent work on the nature of human ecodynamics, this article examines the character and role of the island’s eco-assets – and its crustaceans, in particular – in the emerging experience economy of eco-tourism, illustrating the tensions and instability underlying the latter and its awkward co-existence with mining and detention operations. In this manner, the article characterises the Anthropocene as the central determinant of the present and of possible futures for the island.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.327
Teacher spread0.298 · 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 designNot applicable
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

Citations7
Published2021
Admission routes1
Has abstractyes

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