Mollusk loves: Becoming with native and introduced land snails in the Hawaiian Islands
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".