Whakatipu te Pā Harakeke: What are the success factors that normalise the use of Māori language within the whānau?
Bibliographic record
Abstract
Despite the language revitalisation efforts of kōhanga reo and kura kaupapa Māori, the Māori language is still endangered. The population of highly proficient speakers is dwindling (Statistics New Zealand, 2013). The Māori language is not a language of everyday use across a range of settings (Te Puni Kōkiri, 2008). Language experts have identified intergenerational transmission as the principal means of evaluating the vitality of a language and a key factor in reversing language shift (Fishman, 1991; Spolsky, 2004). This requires re-establishing the Māori language in the home. Although there is evidence of the re-emergence of intergenerational Māori language transmission, this is at the initial stages and is not yet the norm in Māori society. The process of transferring the Māori language from generation to generation depends on decisions by parents to learn and use te reo Māori on an everyday basis in their interactions with their children. Whilst educational institutions can support whānau and communities, they cannot take their place (Fishman, 1991). Community support is vital because a living language requires a pool of active speakers, in particular those who speak the language to younger community members. This thesis examines the efforts of eight whānau who have contributed to the revitalisation of the Māori language by ensuring the language is transmitted intergenerationally to their children. All but one of the parents learnt Māori as a second language in their adult years. Six critical success factors emerged from the findings that can be utilised by language planners and parents wanting to normalise the use of Māori within the whānau. The factors include critical awareness, family language policy, Poureo, support, resources and increasing parental language skills.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".