Bibliographic record
Abstract
Increasing immigration to New Zealand is part of a global rise in cross-border movements. This has led to changes in the nation's multi-ethnic demographic profile and has exerted a pressure to reimagine both national and migrant identities in the context of a society founded on a treaty that promised Māori self-determination. However, in reality its institutions, including the 'mainstream' media, continue to reflect the unequal power relations between Māori and Pākehā resulting from its settler colonial past. New Zealand's indigenous broadcaster, State-funded Māori Television with its mission to revitalise Māori language and culture, sits within this context to offer its audiences Māori images, stories and discourses as a counter to the ideology of 'mainstream' media. Using Māori Television as a case study, I consider the extent to which its programming provides cultural resources for relatively recent migrants to understand a Māori world and navigate issues of identity and national belonging. I have used a combination of both qualitative and quantitative methods, including surveys, focus group discussions and interviews with different geographically based groups of self-selected migrants who had little previous exposure to Māori Television. They were asked to compare what they saw as the representation of Māori on Māori Television with the representations of Māori by the mass media in New Zealand. Focus groups viewed Māori Television for two months, both live-to-air and on-demand, and discussed their ideas about these differences within both face-to-face and on-line Facebook groups. While the nature of cross-cultural interpretations is complex, as neither content nor audiences can be separated from their cultural context, several studies have revealed that shared understandings do emerge when audiences engage with media embedded with other cultural messages. The key cultural resources gained from their viewing experiences were identified by the participants as Te Reo learning opportunities, recognising affinities with Māori cultural concepts, an appreciation of the Treaty in Aotearoa New Zealand as the basis for the assertion of indigeneity, and new ways to consider issues of identity and national belonging. My findings are consistent with research from Canada and Australia that highlights the ability of indigenous media to build bridges of cultural understandings 'over the airwaves' with non-indigenous audiences. The outcomes of my research may be of interest in the future to funding bodies concerned with contributing to migrants' emotional settlement issues and/or asserting the role of indigeneity in Aotearoa New Zealand.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".