The harakeke — No place for the bellbird to sing: Western colonization of Maori art in Aotearoa
Bibliographic record
Abstract
When Cyclone Bola hit I travelled down to the East Coast. Past Opotiki the willow trees were a great mess and the harakeke were buried under the silt. The trees would never resuscitate but when I went back a fortnight later the rito were all standing up out of the silt. I haven't had that much contact with indigenous people (only those of North America, Canada and Hawaii), but it seems to me that those people have not fared as well with their culture, that they have lost a lot more than us. I think that is because we have learnt to be flexible. To survive under the mud and to bide our time and reemerge. The strongest thing that really meant was that we have retained life in our culture like the roots of the harakeke. It's like the wairua. Anybody who tries to separate the roots of a well-established harakeke plant has got a hard job ahead of them. (Toi Maihi, Maori weaver)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".