Drab and distant birds are studied less than their fancy-feathered friends
Bibliographic record
Abstract
Abstract Human decisions are influenced by implicit biases, and scientists do not exist in an objectivity vacuum. Subconscious biases in scientists’ choices about which species to study may beget distorted knowledge bases and stagnant paradigms. Disparities in biological knowledge can result from bias in study species selection within a cycle of policymaking, funding, and publication, all subject to implicit biases. Here, we show that ornithological research in the USA and Canada is biased toward birds with greater aesthetic salience and those with larger breeding ranges and ranges that encompass more universities. We quantified components of aesthetic salience (e.g., color, pattern/contrast, body size) of 293 passerines and near-passerines based on empirically documented human visual preferences and investigated whether these components were associated with research effort. We also quantified each species’ breeding range size and the number of universities within that range. Accounting for phylogenetic relatedness, we found that these metrics of aesthetics, familiarity, and accessibility combined to explain 45% of the variation in the number of published papers about each species from 1965–2020. On average, birds in the top 10% of aesthetic salience were studied 3.0X more than birds in the bottom 10%, and publication numbers were predicted most strongly by color and pattern components of aesthetic salience. Birds in the top 10% of breeding range size and university abundance were studied 3.8X and 3.5X more often than species in the bottom 10% of those categories, respectively. Species listed as Endangered and those featured on journal covers have greater aesthetic salience scores than other species. We discuss how these biases may influence perceived relative value of species with respect to culture and conservation. The disparities in empirical knowledge we describe here perpetuate a positive feedback loop, thus widening the gap between the avian “haves” and “have-nots”, with some questions answered repeatedly while potentially critical discoveries are left undiscovered. “All animals are equal, but some animals are more equal than others.” —George Orwell, Animal Farm (1945)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".