Dataset Information

What's in a name? Taxonomic and gender biases in the etymology of new species names.

ABSTRACT: As our inventory of Earth's biodiversity progresses, the number of species given a Latin binomial name is also growing. While the coining of species names is bound by rules, the sources of inspiration used by taxonomists are an eclectic mix. We investigated naming trends for nearly 2900 new species of parasitic helminths described in the past two decades. Our analysis indicates that the likelihood of new species being given names that convey some information about them (name derived from morphology, host or locality of origin) or not (named after an eminent scientist, or for something else) depends on the higher taxonomic group to which the parasite or its host belongs. We also found a consistent gender bias among species named after eminent scientists, with male scientists being immortalized disproportionately more frequently than female scientists. Finally, we found that the tendency for taxonomists to name new species after a family member or close friend has increased over the past 20 years. We end by offering suggestions for future species naming, aimed at honouring the scientific community's diversity and avoiding etymological nepotism and cronyism, while still allowing for creativity in crafting new Latin species names.

SUBMITTER: Poulin R

PROVIDER: S-EPMC9091844 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

What's in a name? Taxonomic and gender biases in the etymology of new species names.

Poulin Robert R McDougall Cameron C Presswell Bronwen B

Proceedings. Biological sciences 20220511 1974

As our inventory of Earth's biodiversity progresses, the number of species given a Latin binomial name is also growing. While the coining of species names is bound by rules, the sources of inspiration used by taxonomists are an eclectic mix. We investigated naming trends for nearly 2900 new species of parasitic helminths described in the past two decades. Our analysis indicates that the likelihood of new species being given names that convey some information about them (name derived from morphol ...[more]

PMID: 35538778

Similar Datasets

Project description:BackgroundThe digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this 'names problem' has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science.ResultsThe TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets.ConclusionsWe show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/.

Project description:BackgroundArtificial intelligence chatbots such as ChatGPT (OpenAI) have garnered excitement about their potential for delegating writing tasks ordinarily performed by humans. Many of these tasks (eg, writing recommendation letters) have social and professional ramifications, making the potential social biases in ChatGPT's underlying language model a serious concern.ObjectiveThree preregistered studies used the text analysis program Linguistic Inquiry and Word Count to investigate gender bias in recommendation letters written by ChatGPT in human-use sessions (N=1400 total letters).MethodsWe conducted analyses using 22 existing Linguistic Inquiry and Word Count dictionaries, as well as 6 newly created dictionaries based on systematic reviews of gender bias in recommendation letters, to compare recommendation letters generated for the 200 most historically popular "male" and "female" names in the United States. Study 1 used 3 different letter-writing prompts intended to accentuate professional accomplishments associated with male stereotypes, female stereotypes, or neither. Study 2 examined whether lengthening each of the 3 prompts while holding the between-prompt word count constant modified the extent of bias. Study 3 examined the variability within letters generated for the same name and prompts. We hypothesized that when prompted with gender-stereotyped professional accomplishments, ChatGPT would evidence gender-based language differences replicating those found in systematic reviews of human-written recommendation letters (eg, more affiliative, social, and communal language for female names; more agentic and skill-based language for male names).ResultsSignificant differences in language between letters generated for female versus male names were observed across all prompts, including the prompt hypothesized to be neutral, and across nearly all language categories tested. Historically female names received significantly more social referents (5/6, 83% of prompts), communal or doubt-raising language (4/6, 67% of prompts), personal pronouns (4/6, 67% of prompts), and clout language (5/6, 83% of prompts). Contradicting the study hypotheses, some gender differences (eg, achievement language and agentic language) were significant in both the hypothesized and nonhypothesized directions, depending on the prompt. Heteroscedasticity between male and female names was observed in multiple linguistic categories, with greater variance for historically female names than for historically male names.ConclusionsChatGPT reproduces many gender-based language biases that have been reliably identified in investigations of human-written reference letters, although these differences vary across prompts and language categories. Caution should be taken when using ChatGPT for tasks that have social consequences, such as reference letter writing. The methods developed in this study may be useful for ongoing bias testing among progressive generations of chatbots across a range of real-world scenarios.Trial registrationOSF Registries osf.io/ztv96; https://osf.io/ztv96.

Dataset Information

What's in a name? Taxonomic and gender biases in the etymology of new species names.

Publications

What's in a name? Taxonomic and gender biases in the etymology of new species names.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets