Bibliographic record
Abstract
Lessons from Research features presentations by the four conference keynote speakers who bring their different training and experience to bear in addressing Aboriginal CED for the 21st century.In their different styles and from diverse perspectives, the keynote speakers encourage us to think beyond the dominant and often comfortable and comforting models of CED that we have inherited.They challenge us to take on new language, new models, and new theories and to learn from places and people that we may not always consider, so that Aboriginal values might again take centre stage in Aboriginal CED.Two of the keynote speakers -David Newhouse, professor of Native and Administrative Studies, Trent University, and Dr. Wanda Wuttunee, professor of Native Studies and director of Aboriginal Business programs, I.H.Asper School of Business, University of Manitoba, both editors of the Journal of Aboriginal Economic Development -are well-known in CANDO circles for their innovative work in Aboriginal CED.The other two, both at the University of Saskatchewan -Dr.James (Sakej) Youngblood Henderson, Research Director, Native Law Centre of Canada, and a leading expert on treaty federalism, and Dr. Marie Battiste, professor of Educational Foundations and leading authority on the preservation of Aboriginal knowledge and decolonizing initiatives -bring refreshing perspectives from the areas of law and education, reminding us that Aboriginal CED cannot be considered in isolation from other areas of intellectual and socio-cultural activity.In his piece, David Newhouse challenges conventional notions of development that have been promoted by governments and
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.124 | 0.065 |
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".