Priced out of belonging? Insufficient concessions on membership fees across international societies in ecology and evolution
Bibliographic record
Abstract
Learned societies, as professional bodies for scientists, are an integral part of the scientific system. However, their membership fees have the potential to be prohibitive to the most vulnerable members of the scientific community. To shed light on how membership fees are structured, we conducted a survey of 182 international learned societies relevant to researchers in ecology and evolution. We found that 83% of these societies offered fee concessions to students, but only 26% to postdoctoral researchers. An average regular membership fee-US$67.8, student fee-US$27.4 (42.7% of the regular fee) and postdoctoral fee-US$42.7 (52.9%). Other types of individual concessions, such as for emeritus, family or unemployed, were rare (2-20%). Of the surveyed societies, 43% had discounts for members from developing countries (Global South). Such discounts were more common among societies located in high-income countries. Societies with a publicly visible commitment to equity, diversity and inclusion were more likely to offer different types of concessions. Currently, fees may prevent researchers from vulnerable and underprivileged groups from accessing multiple professional benefits offered by learned societies in ecology and evolution. This includes postdoctoral researchers, who should receive more support. We recommend tangible actions towards making learned societies more affordable and accessible.
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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 | 0.026 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".