Bibliographic record
Abstract
As of 31 December 2022, ASLO had 3164 active members which is a decline of 10% relative to 2021. The greatest declines came from the Early Career (−147 for numbers) and Emeritus (−25% for percentages) categories. As with prior years, Student membership exhibited high year-to-year variability in terms of overall numbers. ASLO's Board discussed the need to enhance the communication of benefits for people who have been members for both short- and long-term time scales. Members are dedicated to ASLO, as evidenced by our renewals (people who actively renewed their existing ASLO membership or previously selected a multi-year membership) increasing for the second consecutive year—of the 3164 members in 2022, 81% were renewals. Retention (people kept from 1 year to the next), was also high (76%) and represented a 6% increase from 2021 that continued our positive trajectory from 2020. Of new members, the majority (63%) were Students, with Regular and Early Career researchers being 17% and 16%, respectively. Regarding membership composition, 2619 (83%) shared their primary scientific field. This was delineated as oceanography (43%), limnology (31%), and both oceanography and limnology (26%), with a high degree of researchers conducting multidisciplinary research spanning biology (2094 members), chemistry (991), geology (315), optics (204), and/or physics (431). Our geographic origin spanned 81 Countries and Territories, encompassing regions grouped as North America (69%), Europe (16%), Asia (6%), Africa (3%), Central and South America (3%), Oceania (2%), and the Middle East (1%). Countries with the most ASLO members were the United States (1947 members), Canada (196), Germany (102), Japan (85), Sweden (59), and Spain (54). In 2022, 80% shared their gender identity as male (55%), female (45%), non-binary (<0.5%), and preferred not to say (<0.5%). We did not have sufficient feedback from our survey to reliably assess other demographic factors, such as how gender identity related to age bracket (<50% of respondents shared information on question fields enabling these determinations). ASLO's Board continues to have extensive discussions on how we can grow our diversity and overall distribution, and we welcome your ideas and feedback. The Board remains optimistic about ASLO's long-term trajectory. Please reach out and learn about the many ways in which you can actively participate in our wonderful society (publications, committees, webinars, outreach, and many, many other activities)!
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.217 | 0.211 |
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".