Accessible scientific conferences for blind and low vision professionals and researchers: a necessary step for achieving STEMM equity
Bibliographic record
Abstract
Scientific conferences and meetings are an integral part of careers in research, medicine, education, and many other professional arenas. Such meetings allow professionals and researchers to share their findings, support expansion of professional networks, enhance professional development, and foster new collaborations. However, barriers throughout the conference experience lead to the exclusion of experts with disabilities, deepening existing inequities in the STEMM (science, technology, engineering, mathematics, and medicine) workforce. This is especially problematic for STEMM experts with blindness or low vision, who remain significantly under-represented throughout the health and science ecosystem.
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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 | 0.066 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".