Ko ngā kaumātua ngā poupou o tō rātou ao: kaumātua and kuia, the pillars of our understanding
Bibliographic record
Abstract
This feasibility study examined innovations in kaupapa Māori (a Māori approach) research methods to explore kaumātua (older Māori men and women) understandings of ageing well. We designed a research pathway that brought together kaupapa Māori methods in the form of noho wānanga (a method of knowledge sharing) with kaumātua and researchers in Tutukaka in 2018. Kaumātua participants were invited as guests in a comfortable and congenial setting to share their experiences of growing older. Our engagement with kaumātua, and our data-gathering and analysis methods provided an effective method for understanding kaumātua well-being. We found that focusing directly on health did not resonate with participants. There was diffidence when kaumātua talked about their own personal health, when compared with their enthusiasm for other parts of their lives. They understood well-being as a holistic process connecting hinengaro (mental health), wairua (the spirit and spiritual health), tinana (physical health) and te taiao (natural environments).
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".