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Record W4220872301 · doi:10.1086/720003

Discussions of the “Not So Fit”: How Ableism Limits Diverse Thought and Investigative Potential in Evolutionary Biology

2022· article· en· W4220872301 on OpenAlexaff
Haley A. Branch, Amanda N. Klingler, Kelsey J.R.P. Byers, Aaron Panofsky, Danielle Peers

Bibliographic record

VenueThe American Naturalist · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophy and History of Science
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersBiotechnology and Biological Sciences Research Council
KeywordsAbleismScope (computer science)Field (mathematics)Nexus (standard)SociologyEpistemologyComparative biologyEugenicsBiologyEnvironmental ethicsEngineering ethicsEvolutionary biologyComputer scienceGenetics

Abstract

fetched live from OpenAlex

AbstractEvolutionary biology and many of its foundational concepts are grounded in a history of ableism and eugenics. The field has not made a concerted effort to divest our concepts and investigative tools from this fraught history, and as a result, an ableist investigative lens has persisted in present-day evolutionary research, limiting the scope of research and harming the ability to communicate and synthesize knowledge about evolutionary processes. This failure to divest from our eugenicist and ableist history has harmed progress in evolutionary biology and allowed principles from evolutionary biology to continue to be weaponized against marginalized communities in the modern day. To rectify this problem, scholars in evolutionary research must come to terms with how the history of the field has influenced their investigations and work to establish a new framework for defining and investigating concepts such as selection and fitness.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.009
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.051
GPT teacher head0.242
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations18
Published2022
Admission routes1
Has abstractyes

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