Atomic Eve: Exploring Science Fiction and Social Media to Increase Interest in Nuclear Energy Among Women
Bibliographic record
Abstract
Is it possible to increase interest in learning more about nuclear science among young women through science fiction? How can the interactive elements of social media advance #STEMINISM? Inspiring greater interest in radiation as an educational topic is important to recruiting the future generation of scientists and is crucial to the ability of Canada and other countries to deploy new nuclear power as part of the low carbon energy mix. This article explores how science fiction and social media could help address gender divides in scientific understanding of radiation and encourage more women and young people to pursue nuclear energy careers. While nuclear power can provide stable and clean electricity to replace fossil fuels, learning about nuclear science may be dismissed by today’s youth as “too boring” to reliably grow the workforce to meet future demands. Gender divides in scientific understanding of radiation include the tendency for more males than females to be employed in the nuclear sector, which reaches back to a more general trend in which females are underrepresented among STEM (science, technology, engineering, and mathematics) graduates. Even fictional depictions of radiation tend to be geared toward audiences or interests that are (at least historically) more identified as “for boys” than “for girls.” Science fiction storytelling provides a promising method of engagement to increase interest in nuclear science and possibly inspire more passion in STEM-oriented career paths among youth; however, strategies for overcoming the gender-biased limitations of the science fiction genre must be developed. This paper explores how science fiction and the social media platform Instagram can be combined to spark interest in nuclear energy as a climate change solution among women and young people. Atomic Eve is a science fiction Instagram superhero whose mission on Earth includes helping to solve the climate change crisis by increasing interest in learning more about nuclear energy. This article presents Atomic Eve as a creative experiment in how STEAM education (science, technology, engineering, arts, and mathematics) could help innovate thinking around the role of public engagement in inspiring more women and younger people to pursue careers in the nuclear energy sector.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".