Classifying Mermaids: Observations on Local Naming and Classification of Dugongs ( <i>Dugong dugon</i> ) among the Lio of Flores Island (Eastern Indonesia)
Bibliographic record
Abstract
Folk biological classifications and the taxonomic schemes of scientific biology have often been conceived as two monoliths that sometimes correspond and sometimes do not—as, for example, when folk zoologists classify whales as fish. The Lio people of Flores Island describe dugongs ( Dugong dugon) as creatures that are half human and half fish, thus, essentially like the European image of mermaids. The characterization relates to a myth, widespread in Southeast Asia, which depicts the animals as deriving from a woman. At the same time, Lio speak of dugongs as, simply, a kind of fish. This apparent inconsistency is reflected in several ways people name dugongs, as well as in sex-differentiable terms and numeral classifiers employed when speaking about the animals. Reviewing different ways Lio describe dugong morphology, this nomenclatural variety is shown to correspond to three complementary models identified as diametric, concentric, and chronological dualism. Finally, I demonstrate how these models are comparable to competing ways of representing relationships among animals in modern biological systematics and discuss the implications of such parallels for ongoing debates about similarity and difference between folk and international biology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".