Me Whita, Kia Whita! Hold Fast to Hasten the Blaze!The Development of an Accelerative Approach toAcquiring te Reo Māori.
Bibliographic record
Abstract
This thesis is motivated by an awareness of the key role that Māori second language adult speakers play in the regeneration of the Māori language. The study provides an analytical description of the development of pedagogical materials for a new method of teaching te reo Māori to adults called ‘Kia Whita!’ (Hasten the Blaze!). ‘Kia Whita!’ is designed to rapidly enhance learners’ ability to communicate in te reo Māori while also developing cultural competence, knowledge and understanding. It is modelled on the Accelerative Integrated Method which was pioneered by Wendy Maxwell in Canada for the teaching of French and English to children. The study explains the theoretical foundations on which ‘Kia Whita!’ is built and articulates the special cultural and linguistic considerations that steered its development. This is an applied linguistic thesis drawing on second language acquisition theory and kaupapa Māori methodology. As a result these materials are cognisant of the intertwining issues and needs around second language acquisition, culture, place and the validation of the stated materials by key Māori stakeholders balanced against the varied needs of the second language learner of Te Reo Māori. Adopting this approach to the development of ‘Kia Whita!’ allows the materials to meet the high standards of effective second language pedagogy; and articulate Māori linguistic and cultural content acceptable to Māori experts while being comprehensible to learners of the language.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".