Impact on species' online attention when named after celebrities
Bibliographic record
Abstract
Celebrities can generate substantial attention and influence public interest in species. Using a large-scale examination of publicly available data, we assessed whether species across 6 taxonomic groups received more page views on Wikipedia when the species was named after a celebrity than when it was not. We conducted our analysis for 4 increasingly strict thresholds of how many average daily Wikipedia page views a celebrity had (1, 10, 100, or 1000 views). Overall, we found a high probability (0.96-0.98) that species named after celebrities had more page views than their closest relatives that were not named after celebrities, irrespective of the celebrity threshold. The multiplicative effect on species' page views was larger but more uncertain as celebrity page-view thresholds increased. The range for thresholds of 1 to 1000 was 1.08 (95% credible interval [CI] 1.00-1.18) to 1.76 (95% CI 0.96-2.80), respectively. The hierarchical estimates for the taxa tended to be positive. The strongest effects were for invertebrates, followed by amphibians, reptiles, fish, and mammals, whereas the weakest effect was for birds at lower page-view thresholds. Our results suggest that naming species after celebrities could be particularly significant for those belonging to taxonomic groups that are generally less popular than others (e.g., invertebrates). Celebrities may further influence the effectiveness of this marketing strategy, depending on their likability and connection to the species named after them. Eponyms may serve as a reminder of the disproportionate power dynamics between populations and some namesakes' untenable actions. However, they also provide an opportunity to recognize remarkable individuals and promote equity, inclusivity, and diversity in taxonomic practice. We encourage taxonomists to examine whether naming threatened species after celebrities could affect conservation support, especially for species that are otherwise typically overlooked by the public.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".