Celestial calendar-paintings and culture-based digital storytelling: cross-cultural, interdisciplinary, STEM/STEAM resources for authentic astronomy education engagement
Bibliographic record
Abstract
In D(L)akota star knowledge, the Sun is known as Wi and the Moon is Han-Wi. They have an important relationship, husband and wife. The pattern of their ever-changing relationship is mirrored in the motions of Sun and Moon as seen from our backyards, also called the lunar phases. The framework of the cultural teaching is storytelling and relationships. Cultural perspectives in astronomy such as this remind us of how indigenous ways of knowing are rooted in inclusion, engagement, and relevancy. Designed by A. Lee in 2007, the Native Skywatchers initiative seeks to remember and revitalize indigenous star and earth knowledge, promoting the native voice as the lead voice. The overarching goal of Native Skywatchers is to communicate the knowledge that indigenous people traditionally practiced a sustainable way of living and sustainable engineering through a living and participatory relationship with the above and below, sky and earth. In 2012 two indigenous star maps were created: the Ojibwe Giizhig Anung Masinaaigan-Ojibwe Sky Star Map (A. Lee, W. Wilson, C. Gawboy), and the D(L)akota star map, Makoce Wicanhpi Wowapi (A. Lee, J. Rock). In 2016, a collaboration with W. Buck of the Manitoba First Nations Resource Centre (MFNRC), produced a third star map: Ininew Achakos Masinikan- Cree Star Map Book. We aim to improve current inequities in education for native young people especially through STEM engagement, to inspire increased cultural pride, and promote community wellness. Presented here will be recently created resources such as: astronomical calendar-paintings and short videos that exist at the intersection of art-science-culture. As we look for sustainable ways to widen participation in STEM, particularly in astronomy education, part of the conversation needs to consider the place for art and culture in STEM.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".