Canadian Astronomy on Maunakea: On Respecting Indigenous Rights
Bibliographic record
Abstract
(Abridged) Canadian astronomy has, for decades, benefited from access to observatories and participating in international consortia on one of the best astronomical sites in the world: Maunakea. However, Maunakea is part of the unceded territory of the Native Hawaiian peoples and has always been of special significance to Hawaiian culture. The use of the summit and its science reserve has created tensions in the past decade, particularly with the development of the Thirty Meter Telescope. A meaningful and respectful response from the International astronomy community is still lacking. It is expected that the LRP 2020 will continue to support Canadian astronomy on Maunakea so a better official statement on the position and involvement of CASCA should be prepared. In this paper we present recommendations, based on the United Nation Declaration for the Rights of Indigenous Peoples, for the Canadian astronomical community to better support Indigenous rights on Maunakea and Hawaii while providing clear guidelines for the astronomical community to participate in activities conducted on Indigenous land. This framework is designed to motivate conversations with Indigenous communities regarding our place on Indigenous lands and our roles, and responsibilities toward the communities we are working with. Furthermore, we propose this framework as a basis for engaging with communities around the world regarding consent for astronomical facilities.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.022 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".