Indigenous writing in Canada, Australia and New Zealand
Bibliographic record
Abstract
Words of power ‘Words are sacred’, Anishinaabe poet and editor Kateri Akiwenzie-Damm reminds us. ‘They can transform. Words can change peoples’ attitudes, their thinking, their construction of reality, their actions. Words can change the world. As can silence’. To understand the literatures of Indigenous peoples in Canada, one must recognize the power of words, especially when their source is rooted in the peoples who have called this land home for thousands of years, and whose voices have all too often been passively ignored or actively silenced since the onset of European invasion in the sixteenth century. In the continuing colonial context of Canada, where Aboriginal peoples make up just over 3 per cent of the entire population, the very existence of Indigenous words is a reminder that this is, indeed, a colonized land, and that its first peoples have not gone away or resigned themselves to silence. If anything, their words – in writing as well as ceremony, song, and performance and visual art – affirm the growing representational strength of Native peoples in this land, a strength born of both expansive vision and continuing struggle. The resulting expressive archive – richly realized and diverse in form, purpose and content – constellates a very different understanding of Canada than that assumed by its settler citizens.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".