Politics of Donald Trump and Jacinda Ardern in the Christchurch Mosque Shootings: A Critical Discourse Analysis
Bibliographic record
Abstract
With 1.8 billion adherents of Islam in the world, Muslims make up about 24% of the world’s population. However, their immigration to some Western countries, in the hope of a better life (Syed & Pio, 2017), has made them minorities in the target countries (e.g. UK 5%, Canada 3%, Australia 2%, USA and New Zealand 1%) (Ahmed & Matthes, 2017, p. 227). One of the major problems that they face in the 21st century is a false negative narrative spreading in these countries that terrorists are always Muslims (Corbin, 2017), leading to hatred towards Muslims (Mogan, 2016). This has had several negative impacts such as formation of anti-Muslim groups, anti-Muslim attacks (Pitter, 2017), bullying of school children because of their faith (Abo-Zena, Sahli, & Tobias-Nahi, 2009; Corbin, 2017), mosque shootings in Canada (“Quebec mosque”, 2017) and more recently in New Zealand (Hunter, 2019). The 15 March 2019 shooting in New Zealand was reported to be two consecutive terrorist attacks at mosques in Christchurch, leaving 50 people dead and some other 50 injured. The gunman declared himself as a White nationalist, referring to President Donald Trump as “a symbol of renewed white identity” (Batrawy, 2019). This caused the attacks to be linked mainly to supremacism and alt-right extremism dominating the Western world, specifically the USA emerging from Donald Trump’s administration. Since his presidential campaign Trump’s right-wing populist ideology is characterized by rhetoric of exclusions targeting minorities including Muslims as a threat while promoting supremacy of the Whites (Giroux, 2017). On the other hand, New Zealand’s Prime Minster Jacinda Ardern who advocates social-democratic party intends to create a society in which inequality is lessened and to ensure that every individual, regardless of their background, feels socially and economically secure, and that people show kindness and understanding toward each other (Ardern, 2018). As the discourse of politicians affects the way people perceive themselves and others, the current study analyses the function and meaning of the strategies employed by the two leaders reacting to the mosque shootings in New Zealand to unravel their ideological stance on cultural hybridity resulting from diasporic encounters.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.037 | 0.048 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".