Racialization of public discourse: portrayal of Islam and Muslims
Bibliographic record
Abstract
Many developed countries like the USA, UK, Canada and European countries have diverse communities, including Muslim community outreach, as a result of immigration turning the world into a global village for all religions. Persecuting any one religion can lead to unrest and damage the calm of the society at large. This study critically examines the trends and research findings on Islamophobic discourse from 2001 to 2022 by investigating how linguistic strategies have been employed to present Muslims and Islam, the racialization of Muslims, the sense of identity crisis, and the way Muslims encounter and resist Islamophobia. An empirical study is also conducted to analyze the media discourse on recent incidents in Canada and New Zealand. For this purpose, 56 research articles are retrieved from the databases of four publishers: Taylor & Francis, Sage Publication, Pluto Journals, and Science Direct. A systematic review methodology and content analysis of the elected articles reveals that qualitative methodology was used in most articles and the UK and the US are the focal countries where most of the Islamophobia studies are carried out. Interviews and print media are found to be the preferred data samples for Islamophobia research. The most common theme in the articles is how anti-Muslim ideology was constructed by painting negative images of Muslims and Islam and subsequently presenting them as 'others.' The multiple effects of Islamophobia was paid considerable attention by the researchers of the reviewed articles. Themes receiving less attention are Islamophobia for political gain, identity crises, and the racialization of Muslims, whereas the factors behind Islamophobia received scant attention. The development of Islamophobia as a topic within the field of critical discourse analysis has been understudied. A critical discourse analysis of two recent incidents in Canada (2021) and New Zealand (2019) shows how various linguistic strategies are employed to construct negative images of Muslims and Islam.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".