Astro2020 APC White Paper: Accessible Astronomy: Policies, Practices,\n and Strategies to Increase Participation of Astronomers with Disabilities
Bibliographic record
Abstract
(Abridged) In this white paper, we outline the major barriers to access\nwithin the educational and professional practice of astronomy. We present\ncurrent best practices for inclusivity and accessibility, including classroom\npractices, institutional culture, support for infrastructure creation, hiring\nprocesses, and outreach initiatives. We present specific ways--beyond simple\ncompliance with the ADA--that funding agencies, astronomers, and institutions\ncan work together to make astronomy as a field more accessible, inclusive, and\nequitable. In particular, funding agencies should include the accessibility of\ninstitutions during proposal evaluation, hold institutions accountable for\ninaccessibility, and support efforts to gather data on the status and progress\nof astronomers and astronomy students with disabilities.\n
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.033 |
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".