Who gets the spotlight? Disparities in seabird research attention at scientific conferences
Bibliographic record
Abstract
Seabirds are among the most threatened bird groups globally, yet research effort is unevenly distributed among species, with some taxa receiving extensive attention while others remain understudied or entirely overlooked. To evaluate patterns of taxonomic and thematic research focus, we reviewed 2962 abstracts from 30 seabird-focused conferences held between 2015 and 2025. Across these abstracts, we recorded 4547 mentions of seabirds and categorized the species and primary research foci for each presentation. Research attention was skewed, with 16 seabird species over-studied, 70 species under-studied, and a further 70 seabird species were never mentioned. There was no significant difference in the proportion of IUCN Red List categories between over- and under-studied taxa. Research topics were dominated by “Tracking,” “Monitoring” and “Threats,” with Common Murre ( Uria aalge ) being disproportionately mentioned. Geographic and taxonomic biases were evident, with under-mentioned or unmentioned species more likely to occur in the Southern Hemisphere. This study highlights an imbalance in seabird representation at conferences and underlines the risk of certain species being excluded from conservation planning due to a lack of ecological data. Addressing these disparities will require deliberate, coordinated efforts to redirect research toward neglected taxa and regions, ensuring seabird science can more effectively support global biodiversity goals.
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 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 | 0.055 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".