Bibliographic record
Abstract
Abstract Of all the sciences, astronomy is by far the most border-less in its activities, and the most advanced in its concepts of collaborating across borders. In their dealings and in their needs, today’s teams are mature enough to ignore gender differences and ethnic differences, and across the past 50+ years of IAU membership which I personally can chalk up, the IAU personnel, Commissions, and other bodies have come to reflect more nearly the same – albeit small – gender ratios as found in its member institutions. In the IAU there has always been space for the individual, and if one recalls the early contributions to the IAU by major players like Edith M¨uller, Giusa Cayrel, Anne Underhill and Charlotte Moore, I think it can be said that astronomy was, and knew it was, better off by giving such people the latitude that they deserved as scientists, rather than because they were women. When a meeting in Baltimore in 1992 was called to discuss “Women in Astronomy”, the pressure came from the younger generations, who feared that the low percentages of tenured women in astronomy would be allowed to continue unnoticed, so they created the Baltimore Charter to draw attention to what certainly appeared to some as discrimination. Even though there could be no quick fixes to the situation, and the winds of change have been more like zephyrs than the cleansing gales that some hoped for, the percentage of women now rising through the ranks is definitely on the increase, and is witnessing growing ethnic diversity. Those are a matter of pride for the IAU, and must be highlighted in this its Centenary Year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".