Do We Need Kiwi Lessons in Biculturalism? Considering the Usefulness of Aotearoa/New Zealand's Pākehā Identity in Re-Articulating Indigenous Settler Relations in Canada
Bibliographic record
Abstract
Abstract Canada is beginning to slowly embrace an ethic of Indigenous-settler biculturalism. One model of change is afforded by the development of biculturalism in Aotearoa/New Zealand, where recent Indigenous Māori mobilization has created a unique model in the Western settler world. This article explores what Canada might learn from the Kiwi experience, focusing on the key identity marker Pākehā, an internalized and contingent settler identity, using Indigenous vocabulary and reliant on a relationship with Indigenous peoples. This article gauges Pākehā’s utility in promoting biculturalism, noting both its progressive qualities and problems in its deployment, including continued inequality, political alienation, and structural discrimination. While Canada has no Pākehā analogue, terms such as “settler” are being operationalized to develop a larger agenda for reconciliation along the lines recommended by the Truth and Reconcilliation Commission. However, such terms function best when channelled towards achieving positive concrete goals, rather than acting as rhetorical screens for continued inaction.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.027 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".