ISLAMOPHOBIA IN CANADA: THE ROLE OF CANADIAN NEWSPAPERS IN SHAPING ATTITUDES TOWARDS ISLAM AND MUSLIMS
Bibliographic record
Abstract
The issue of Islamophobia got attention from researchers, academicians and journalist for the last three decades However the issue got enormous attention after the Twin Towers were attacked on 9/11, 2001, in USA. This study examines how media portray the issue of Islamophobia. For that purpose, two leading newspapers of Canada namely Globe and Mail and Toronto Star selected for content analysis. This study uses content analysis by selecting the articles published on Islamophobia in two leading newspapers of Canada namely The Toronto Star and The Globe & Mail for a period of three months (January to March 2017). There are several parts of a newspaper that are analyzed (i.e. stories, content framing, priming and overall structure). In the first step, we go through the newspaper and identify all the stories. In the second stage, we identify the framing and priming of the stories and articles on Islamophobia. The main focus of this study is to examine the framing of the stories related to Muslims and Islamophobia in Toronto Star and The Globe & Mail newspapers. Moreover, this study concentrated on issue of Islamophobia before and after the Quebec Mosque attack. The main results of the study show that during this period, the negative stories published on Islamophobia are more evident from Toronto Star and Globe and Mail newspaper despite the fact that both newspapers are liberal according to their theme of the stories. As compare to Globe and Mail, Toronto Star published more articles on Islamophobia. Keywords: Islamophobia, Attitudes, Content Analysis
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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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".