Does science drive species naming, or can species naming drive science? A perspective from plant-feeding arthropods
Bibliographic record
Abstract
Abstract How do researchers choose their study species? Some choices are based on ecological or economic importance, some on ease of study, some on tradition – but could the name of a species influence researcher decisions? We asked whether phytophagous arthropod species named after their host plants were more likely to be assayed for host-associated genetic differentiation (or ‘HAD’; the evolution of cryptic, genetically isolated host specialists within an apparently more generalist lineage). We chose 30 arthropod species (from a Google Scholar search) for which a HAD hypothesis has been tested. We traced the etymologies of species names in the 30 corresponding genera, and asked whether HAD tests were more frequent among species whose etymologies were based on host-plant names (e.g., Eurosta solidaginis ) vs. those with other etymologies (e.g., Eurosta cribata ). Species with host-derived etymologies were more likely to feature in studies of HAD than those with other etymologies. We speculate that the etymology of a scientific name can draw a researcher’s attention to aspects of life-history and thus influence the direction of our scientific gaze.
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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 | 0.076 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".