Multicultural Education in New Zealand: Suggestions to Resolve the Tensions between Biculturalism and Multiculturalism
Bibliographic record
Abstract
New Zealand has become an increasingly multi-ethnic nation since major changes to its immigration policy in the 1980s. However, this development has raised concerns among Maori interest groups who have viewed various governments' attempts to achieve a wider multiculturalism as detrimental to biculturalism, and a loss of their identity and influence as guaranteed under the Treaty of Waitangi. This thesis proposes that the development of clear education policies based on "critical (effective) multicultural education" (CEME) is necessary in New Zealand because such education aims to create social cohesion and social and educational equality among various ethnic groups. This research critiques the reasons why New Zealand has been unable to implement CEME, first by examining the historical development of Maori education, and by reflecting on recent attitudes towards biculturalism. It then examines government attempts to achieve a wider multiculturalism through the introduction of "Taha Maori" (literally, "the Maori side") into the curriculum. It concludes that this has failed because it does not acknowledge the national minority rights of Maori as guaranteed in the Treaty of Waitangi, thus creating serious tensions between the advocates of biculturalism and those of multiculturalism. Multiculturalism of this type cannot be considered CEME as it tends to maintain the imbalance of power between minority ethnic groups vis-à-vis the majority group in a society. It ultimately fails to achieve social and educational equality among these groups. Canada, in contrast, appears to have successfully created social cohesion as well as social and educational equality among its various ethnic groups by operationalizing its multicultural policies in a climate of strong biculturalism. This thesis therefore outlines how that country has implemented its multicultural policies and operationalized them through such means as programmes for French language immersion, heritage language bilingualism, and heritage language maintenance. By analysing how Canada has managed to reduce the tensions between biculturalism and multiculturalism, this research makes recommendations for how the New Zealand government and the country's schools and teachers can effectively implement CEME through the introduction of additive bilingual education programmes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".