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Record W4288283876 · doi:10.48550/arxiv.1907.04943

Astro2020 APC White Paper: Accessible Astronomy: Policies, Practices,\n and Strategies to Increase Participation of Astronomers with Disabilities

2019· preprint· W4288283876 on OpenAlexaff
Alicia Aarnio, Nicholas A. Murphy, Karen Knierman, Wanda Diaz Merced, Alan Strauss, Sarah Tuttle, Jacqueline Monkiewicz, Adam J. Burgasser, Lía Corrales, Mia Sauda Bovill, Jason Nordhaus, Allyson Bieryla, Patrick Young, J. Noel-Storr, Jennifer Cash, Nicole Cabrera Salazar, Hyunseop Choi

Bibliographic record

VenuearXiv (Cornell University) · 2019
Typepreprint
Language
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsOutreachWhite paperWork (physics)Political scienceWhite (mutation)Grant fundingPublic relationsPhysicsPublic administrationLawChemistry

Abstract

fetched live from OpenAlex

(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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.108
GPT teacher head0.284
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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Same venuearXiv (Cornell University)Same topicDisability Education and EmploymentFrench-language works237,207