The "standard story" of anti-Māori talk in Pae Ora (Healthy Futures) Bill submissions.
Journal
The New Zealand medical journal
ISSN: 1175-8716
Titre abrégé: N Z Med J
Pays: New Zealand
ID NLM: 0401067
Informations de publication
Date de publication:
21 Jul 2023
21 Jul 2023
Historique:
medline:
31
7
2023
pubmed:
28
7
2023
entrez:
28
7
2023
Statut:
epublish
Résumé
To review some common patterns of race talk in a sample of submissions made to the Pae Ora (Healthy Futures) Bill. This bill proposed a structural reform of the health system in Aotearoa New Zealand to address long-standing health inequities experienced by Māori, the Indigenous peoples, and other priority populations. In a sample of 3,000 individual submissions made in late 2021, we found 2,536 explicit references to race. Utilising the "standard story" frame of Pākehā/non-Maori race talk, five longer submissions that inferred that the Pae Ora bill was "racist" were analysed in detail. Many "standard story" race discourses were identified in the Pae Ora submissions. Three derived discourses included in this paper are: Pākehā as norm (monoculturalism or not seeing Pākehā as a culture), equality and the "Treaty" (equality for all to access healthcare), and one people (we are all New Zealanders). Sources such as the Waitangi Tribunal Wai 2575 Hauora report were drawn on to provide alternative discourses. Identifying Pākehā standard story discourses enables learning about language patterns systems draw on, and the development of tools and procedures to improve equity for Māori and eliminate institutional racism.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
62-69Subventions
Organisme : The authors' involvement in this work is financially supported by the 2020 MBIE Endeavour Fund for the Working to End Racial Oppression (WERO) programme, and Waikato Public Health Service.
Informations de copyright
© PMA.
Déclaration de conflit d'intérêts
The funders had no roles in study design and collection, analysis, and interpretation of data.