The giant, the wintermaker, and the hunter: contextual ethnoastronomy towards cultivating empathy
Bibliographic record
Abstract
Abstract ‘It is the belt!’ This is how middle school teachers in a science teaching professional development program rationalized why they believe Orion is the most recognizable of all constellations in the night sky. It was from this foundation that we chose Orion to be the focus of a four-phase ethnoastronomy-based project reported here. Ethnoastronomy, within this context, can be described as the study of myths as bearers of cultural knowledge from oral traditional societies (Lankford 2007 Reachable Stars: Patterns in the Ethnoastronomy of Eastern North America (Tuscaloosa, AL: University of Alabama Press)). After identifying the stars comprising the ‘belt’, and the greater asterism through the singular and oversimplified lens of the Greek mythology that dominates our astronomy curricular learning objectives, we explored deeper through Ojibwe, Arab, and Jewish perspectives as these cultures and religions were represented in our group. The purpose of this paper is to share insights emanating from emergent critical questions generated in our professional development program regarding where, how, and why we should introduce traditional cultural knowledge that complements Western skylore into our classrooms towards helping to cultivate empathy for others in our community.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.056 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".