Indigenous Science Network Bulletin - November 2021
Bibliographic record
Abstract
Board Chair, Liz McKinley has written an inspiring editorial that reflects on her journey as a Maori science educator working across all levels in a career spanning over 40 years. Liz also touches on one of the long running controversies that seems unavoidable in this space, that of the value and credibility of allowing non-western cultural knowledge into science curricula and pedagogy. The program of the second biennial Turtle Island Indigenous Science Conference, held at the University of Regina in Canada is available. This issue also contains three items written specifically for us. The first is a summary of the Queensland Education Department’s Solid Pathways program by Dr Hind Hegazy, which see online STEM instruction provided to First Nations primary level students. The second is an item provided by another staff member of Queensland Education, being Goodna State School’s science teacher Gerard Salmon. He provides opportunities for Indigenous girls to excel at science through an Indigenous Girls' Technology and Drone Club. The third is provided by Dr Nick Ruddell of Charles Sturt University. Along with colleague Holly Randell-Moon, they have summarised a recently published book chapter titled Country as teacher in the development of cross-cultural Indigenous science environmental education.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.374 | 0.189 |
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".