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Record W4388128686 · doi:10.1098/rspb.2023.1970

Naming the menagerie: creativity, culture and consequences in the formation of scientific names

2023· review· en· W4388128686 on OpenAlexaff
Stephen B. Heard, Julia J. Mlynarek

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsEspace pour la vieUniversity of New Brunswick
Fundersnot available
KeywordsMenagerieCreativitySociologyAnthropologyEpistemologyPsychologyHistoryPhilosophySocial psychologyArt history

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.845
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.012
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.136
GPT teacher head0.359
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations22
Published2023
Admission routes1
Has abstractyes

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