When Is a Fish Not a Fish? Questions Raised by a Nage Life-Form Category
Bibliographic record
Abstract
Speakers of a Central-Malayo-Polynesian language, the Nage inhabit the central region of the eastern Indonesian island of Flores. Their folk taxonomy of animals (ana wa) contains three named life-form taxa, one of which is ika, fish. A review of component folk-generic taxa, however, reveals that Nage do not classify five kinds of freshwater fish as ‘fish’ (ika), even though they further apply ika to various marine fish (including sharks and rays) as well as to marine mammals. The article considers this peculiarity of Nage folk zoological taxonomy, and how it might affect an understanding of ika as denoting a ‘fish’ life-form taxon. The main conclusion is that the five excluded categories—distinguished largely on morphological and behavioural grounds, and conveniently designated as the ‘tebhu cluster’, after one of their members—are contrasted primarily with freshwater species which Nage do classify as ‘fish’ (ika). Specified by name as ika lowo (‘river fish’), these are further contrasted with another named folk-intermediate taxon of ‘marine fish’ (ika mesi). From this, it is argued that, as a life-form category, ika should be understood as implicitly including the five members of the ‘tebhu cluster’ as a third, albeit covert, folk-intermediate taxon.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".