Naming the menagerie: creativity, culture and consequences in the formation of scientific names
Bibliographic record
Abstract
The coining of scientific names for newly described species is one of the most creative acts in science. We briefly review the history of species naming, with an emphasis on constraints and freedoms in the choice of new names and how they came to be. We then consider patterns in etymologies and linguistic origins of scientific names across clades and through time. Use of 'non-classical' languages (those other than Latin and Greek) in naming species has increased, as has the use of eponymous names (despite recent controversy around the practice). Finally, we consider ways in which creativity in naming has consequences for the conduct and outcome of scientific work. For example, sale of naming rights has funded research and conservation, while naming species after celebrities has increased media attention to the science of species discovery. Other consequences of naming are more surprising, including a strong effect of species-name etymology on the kinds of scientific studies conducted for plant-feeding arthropods. Scientific naming is a clear example of how science and scientists are socially situated, and how culturally influenced decisions such as what to name a new species can affect both public perception of science and the conduct of science itself.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".