Limited Progress in Improving Gender and Geographic Representation in Coral Reef Science
Bibliographic record
Abstract
Despite increasing recognition of the need for more diverse and equitable representation in the sciences, it is unclear whether measurable progress has been made. Here, we examine trends in authorship in coral reef science from 1,677 articles published over the past 16 years (2003–2018) and find that while representation of authors that are women (from 18 to 33%) and from non-OECD nations (from 4 to 13%) have increased over time, progress is slow in achieving more equitable representation. For example, at the current rate, it would take over two decades for female representation to reach 50%. Given that there are more coral reef non-OECD countries, at the current rate, truly equitable representation of non-OECD countries would take even longer. OECD nations also continue to dominate authorship contributions in coral reef science (89%), in research conducted in both OECD (63%) and non-OECD nations (68%). We identify systemic issues that remain prevalent in coral reef science (i.e., parachute science, gender bias) that likely contribute to observed trends. We provide recommendations to address systemic biases in research to foster a more inclusive global science community. Adoption of these recommendations will lead to more creative, innovative, and impactful scientific approaches urgently needed for coral reefs and contribute to environmental justice efforts.
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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 | 0.076 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".