The future of scientific societies
Bibliographic record
Abstract
The future of scientific societies AAAS (the publisher of Science) turns 175 years old this year.AAAS's mission is to advance science, engineering, and innovation throughout the world for the benefi t of all.To celebrate this milestone and explore AAAS's anniversary theme of "igniting progress for the next 175," we asked young scientists, "How have scientifi c societies aff ected your career, and how can societies best support scientists in the future?"Read a selection of the responses here.Follow NextGen Voices on Twitter with hashtag #NextGenSci.-Jennifer Sills InclusionScientific societies have given me the opportunity to attend events, enter contests, and start a science communication project.Ensuring more representation of neurodivergent researchers and other minorities in scientific societies is what drives me, as an autistic researcher, to pursue my goals.Scientific societies' most important role in the future will be inclusion.When scientific societies care about sharing the work and struggles of Black, LGBTQIA+, female, neurodivergent, and other underrepresented researchers, they create a more welcoming scientific community, which will encourage more individuals in minority groups to become scientists.
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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 | 0.028 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.066 | 0.080 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".