Bibliographic record
Abstract
Here I examine the case of the Māori in New Zealand, which provides me with a second case study of how processes of declining and then increasing values for tribal land has affected ethnic identity. As in the USA, population growth and subsequent urbanization in the mid-twentieth-century led to a rise in pan-Māori nationalism, with evidence that native language loss in cities did not halt the rise of Māoritanga (Māori-ness). However, judicial rulings that attempted to compensate the Māori for their historical loss of land and livelihoods gave resources to individual iwi (tribes) rather than the Māori community as a whole, which has had led to a renewed emphasis on iwi identity above and beyond a common Māori identity. In particular I focus on fisheries policy that has allocated money to iwis according to their coastline length and show that those iwi with longer coastlines have seen higher population growth in recent censuses. I conclude the chapter with a brief examination of indigenous peoples in both Australia and Canada, where I show that industrialization has induced assimilation into pan-tribal identities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".