Equitable and Inclusive Public Outreach with the James Webb Space Telescope: Combining Art, Science, and Technology
Bibliographic record
Abstract
NASA's James Webb Space Telescope revealed its first images on July 12, 2022 and has been doing groundbreaking science ever since. Communication is essential for involving the public in NASA's discoveries of our universe. Scientific exploration transcends international borders; it united humanity to make a mission like Webb feasible in collaboration with the European Space Agency (ESA) and the Canadian Space Agency Art is a medium that can transcend the boundaries of language, culture, and ability. It can serve as a bridge for communication between technical and non-technical communities through its use of different avenues to express the beauty and nuance of science and engineering in ways that may not be readily accessible to those outside of technical communities of practice. However, equitable and inclusive outreach endeavors are challenging to implement while considering language translations, culture context, sensory methods, and technology capabilities. Formats including the "Unfolding the Universe'' virtual reality (VR) platform have allowed for a wider audience to interact with Webb's images, including sounds, visual aids, and talks by the scientists and engineers who worked on Webb. This VR platform has been expanded to highlight the ESA contributors to the mission and will be showcased internationally. Webb's first images were also translated into sounds as a method of inclusion for those with sight disabilities. We will explore some successful outreach methods and provide suggestions for inclusivity in future.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.025 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".