Bibliographic record
Abstract
Indigenous researchers are knowledge seekers who work to progress Indigenous ways of being, knowing and doing in a modern and constantly evolving context. This book describes a research paradigm shared by Indigenous scholars in Canada and Australia, and demonstrates how this paradigm can be put into practice. Relationships don’t just shape Indigenous reality, they are our reality. Indigenous researchers develop relationships with ideas in order to achieve enlightenment in the ceremony that is Indigenous research. Indigenous research is the ceremony of maintaining accountability to these relationships. For researchers to be accountable to all our relations, we must make careful choices in our selection of topics, methods of data collection, forms of analysis and finally in the way we present information. I’m an Opaskwayak Cree from northern Manitoba currently living in the Northern Rivers area of New South Wales, Australia. I’m also a father of three boys, a researcher, son, uncle, teacher, world traveller, knowledge keeper and knowledge seeker. As an educated Indian, I’ve spent much of my life straddling the Indigenous and academic worlds. Most of my time these days is spent teaching other Indigenous knowledge seekers (and my kids) how to accomplish this balancing act while still keeping both feet on the ground.
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 | 0.050 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".