Taxonomic practice, creativity and fashion: what’s in a spider name?
Bibliographic record
Abstract
Abstract There is a secret pleasure in naming new species. Besides traditional etymologies recalling the sampling locality, habitat or morphology of the species, names may be tributes to some meaningful person, pop culture references and even exercises of enigmatography. Using a dataset of 48 464 spider etymologies, we tested the hypothesis that species names given by taxonomists are deeply influenced by their cultural background. Specifically, we asked whether naming practices change through space or have changed through time. In absolute terms, etymologies referring to morphology were the most frequently used. In relative terms, references to morphology peaked in 1850–1900 and then began to decline, with a parallel increase in etymologies dedicated to people and geography. We also observed a dramatic increase in etymologies referring to pop culture and other cultural aspects in 2000–2020, especially in Europe and the Americas. While such fashionable names often carry no biological information regarding the species itself, they help give visibility to taxonomy, a discipline currently facing a profound crisis in academia. Taxonomy is among the most unchanged disciplines across the last centuries in terms of tools, rules and writing style. Yet, our analysis suggests that taxonomists remain deeply influenced by their living time and space.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".