Te ao Māori o te Wānanga Takiura – He ao wairua, he wairua tuku iho
Bibliographic record
Abstract
He rahi ngä kupu körero mö tënei ähua o te ao wairua kei roto i te ao Mäori.He hokinga ki te nehenehe nui o te ao ätea, ki töna kunenga mai ki te ao türoa.Ko tana whakapapa i ahu mai i te orokohanga: Te Räkau o Te Ao | Te Ao Mätinitini = Te Rangi Mätinitini | Ngä atua nui tonu (70) (Williams, K. K. & T. T. R., 1980) Ka wehea e Tane-nui-a-rangi öna matua, kätahi ka pätere mai te karakia hai whakapiri i a Ranginui ki te rangi, hai whakatakoto kia mau a Papatüänuku ki te whenua.Kia pümau ai ënei tokorua ki ö räua ake rohe, ka whakahaeretia ngä karakia o te kete tuauri kia ito te hono o Rangi ki runga, ko Papa ki raro.Nä tërä, i uru mai ai te wairua o ënei karakia ki te ao türoa.Nä ngä mahi wänanga me ngä pakanga a ngä atua i rohengia ai te whenua me ngä moana o tënei ao.Tënä atua ki töna rohe, tënä atua ki töna rohe.Ko te ira tangata noa i te ngaro.
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 | 0.000 | 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 | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".