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Record W2979796746 · doi:10.48550/arxiv.1910.02976

Indigenizing the next decade of astronomy in Canada

2019· preprint· en· W2979796746 on OpenAlexaboutno aff
Hilding R. Neilson, Laurie Rousseau-Nepton, Samantha Lawler, Kristine Spekkens

Bibliographic record

VenuearXiv (Cornell University) · 2019
Typepreprint
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousObligationCommissionInclusion (mineral)Political scienceWork (physics)Library scienceSociologySocial scienceEngineeringLawEcologyComputer science

Abstract

fetched live from OpenAlex

(Abridged) The Truth and Reconciliation Commission of Canada published its calls to action in 2015 with 94 recommendations. Many of these 94 recommendations are directly related to education, language, and culture, some of which the Canadian Astronomy community can address and contribute to as part of reconciliation. The Canadian Astronomy community has an additional obligation since it benefits from facilities on Indigenous territories across Canada and the world. Furthermore, Indigenous people are still underrepresented at all levels in Canadian astronomy. The purpose of this Community Paper is to develop recommendations for the Canadian astronomy community to support Indigenous inclusion in the science community, support Indigenous learning by developing Indigenous-based learning materials and facilitate access to professionals and science activities, and to recognize and acknowledge the great contributions of Indigenous communities to our science activities. As part of this work we propose the ten following recommendations for CASCA as an organization and throughout this Community Paper we will include additional recommendations for individuals: astronomers, students and academics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.823
Threshold uncertainty score0.955

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0270.008
Scholarly communication0.0170.004
Open science0.0030.006
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0260.003

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.

Opus teacher head0.061
GPT teacher head0.212
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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Same venuearXiv (Cornell University)→Same topicIndigenous Health, Education, and Rights→French-language works237,207→