What Can Professional Scientific Societies Do to Improve Diversity, Equity, and Inclusion: A Case Study of the American Elasmobranch Society
Bibliographic record
Abstract
Scientific professional societies are reviewing diversity, equity, and inclusion (DEI) practices and policies in response to recent calls for much-needed change. Organizations like scientific professional societies contribute to establishing disciplinary norms, and can influence the diversity of disciplinary workforces in multiple ways through both action and inaction. This paper examines these issues using the American Elasmobranch Society (AES), a medium-sized professional scientific society, as a case study. It consists of three parts: (1) an analysis of the demographics of AES members, leaders, and award winners; (2) an evaluation of a diversity initiative created by the society which includes a survey of program alumni focusing on potential improvements to the program; and (3) a synthesis of recommendations of steps that AES and similarly sized societies can take to better support DEI goals. AES’s membership in recent years is more than half women, but 71.5% of all leadership positions in the Society’s history (including all but two Presidents) have been held by men since the society was founded in 1983. AES’s membership has significantly fewer Black/African-American members than the United States scientific workforce overall, with just 1 member out of over 400 identifying as Black in 2019, and 86.6% of Society leadership positions have been held by white-presenting members. The Society’s diversity initiative has led to some limited professional benefits for awardees, but could benefit from additional resources and support to enact suggested expansions and improvements. We provide a series of actionable recommendations that will make the annual meetings of societies like AES, and the field of chondrichthyan science, safer and more inclusive.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.026 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".