Bias in American Ornithologists' Union Bird Names
Bibliographic record
Abstract
Ornithology has developed bodies to make collective decisions on the taxonomy, scientific names, and common names of birds. This tradition within ornithology assists with communication and reduces confusion. For North and Central America, a committee of the American Ornithologists' Union standardizes the taxonomy and nomenclature of all the birds that naturally occur within that area. This paper makes the point that this activity has been dominated by members from the United States, with insufficient attention paid to the appropriate use of the term "American" or to the concerns of citizens of countries other than the USA. As a result, the term "American" is used inappropriately as a synonym for North American in a geographic distribution sense. In addition, the terms "Canadian" and "Mexican" are used very sparingly or not at all in the English common name for species that occur in those countries. Suggestions are made with regards to the membership of the nomenclature committee and for remedying this problem with English common names.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".