Why the term Africanized bees is problematic in a racist society
Bibliographic record
Abstract
Several key words used in beekeeping are racially charged: in order to make beekeeping and ecology more inclusive, we need to replace them.<br>Several words used to describe honey bees directly reference the traumatic history of slavery in the United States. It is important to consider alternatives to these loaded and painful terms to make careers in the bee world more welcoming to people of color and to reach a broader audience as we communicate ideas about ecology and beekeeping. We borrow the concept of social reflection from the field of Environmental Sociology to explain how terms that humans use to describe nature often reflect the implicitly racist society in which they were developed (in the United States). This begins to explain how ecologists and beekeepers may use words that reinforce white supremacy without harboring racist intentions. We focused on one: calling hybrids of the sub-species <i>Apis m. scutellata</i> “Africanized”. Scholars and the general public describe Africanized "killer bees" as “more violent” than European bees, and beekeepers “worry” that these Africanized bees will mate with their European queens. This description is dangerously similar to stereotypes against African Americans that white people have long used to justify racial oppression. We need to replace the terms “African” and “Africanized” in favor of more biologically-accurate words like “Equatorial” or "s<i>cutellata." </i> Recognizing how scientific discourse is constructed opens the space to imagine alternatives to critical terms that are rooted in the United States’ history of slavery. These terms reinforce damaging racial stereotypes. Shifting our language, and selecting our words with care is a practical and powerful step we can take towards making bee science and beekeeping a more socially just practice.<br><br>This talk was given at the international beekeeping conference Apimondia, in Montreal in September 2019.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.761 | 0.093 |
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; both teacher heads agree on what is shown here.
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".