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Record W4226166204 · doi:10.24043/isj.383

Mollusk loves: Becoming with native and introduced land snails in the Hawaiian Islands

2022· article· en· W4226166204 on OpenAlexvenueno aff
Jonathan Galka

Bibliographic record

VenueIsland Studies Journal · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicPaleopathology and ancient diseases
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEcologyLand snailGeographyIntroduced speciesBiologySnail

Abstract

fetched live from OpenAlex

Hawaiian island land snails once represented one of the most diverse archipelagic evolutionary radiations. Historically, indigenous Hawaiians (Kānaka maoli) and Westerners also heard some snails (kāhuli) sing. Today, most of these species are extinct or endangered. One major cause has been the intentional mid-20th century introduction of a land snail, Euglandina rosea, for the biological control of another mollusk, Lissachatina fulica. In this article, I join efforts of noticing and engaging landscapes of the situated Anthropocene with the goal of demonstrating the potential for mollusks to be dynamic alliance-forming companions. In articulating methods of becoming with snails, I pass kāhuli through Western and Kānaka maoli knowledge-making projects. First considering the evolutionary biological work of John Gulick and his counterparts to genealogize contemporary snail-love, I then elaborate on what care and hope might mean with Pacific Island land snails living through ongoing environmental dispossession and alteration. I then reconsider Euglandina on parallel conceptual terms, engaging natural historical and laboratory accounts to think with the introduced mollusk beyond its categorization as ‘alien invader’. Loving Euglandina as well as kāhuli may help realize livable futures for indigenous and introduced Hawaiian island mollusks alike, in a world hopefully full of snail-song.

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)0.000
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 score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.270
Teacher spread0.226 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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