Bibliographic record
Abstract
Abstract Islamophobia is widely known as the fear of Muslims and Islam. However, there is more to this terminology than just its literal translation. The term Islamophobia itself has been widely debated by scholars over its definitions and use. While many scholars agree that Islamophobia refers to the negative treatment of Muslims and the misrepresentation of Islam, notions of anti-Muslimness are debated. Islamophobia also effects non-Muslim communities and individuals who are perceived to resemble Muslims, such as non-Muslim Arabs, Sikhs, Latine, and other minority religious and ethnic groups. So, what exactly constitutes Islamophobia? Is Islamophobia different than anti-Muslimness or anti-Muslim bigotry? Does Islamophobia refer to the fear/hate of Muslims as people or is it directed toward Islam as a religion? Where does Islamophobia stem from? Understanding Islamophobia, along with its roots and causes, is significant to further explore its impacts on Muslim communities where research is lacking in North America such as in Latin America or the Caribbean. Studying Islamophobia also benefits those who aim to combat the discrimination, prejudice, and bigotry that Muslim communities and individuals face whether they are challenging anti-Muslim laws or campaigning for anti-Islamophobia education. Factors that contribute to advocacy of anti-Muslim hate and fear include politicians with anti-Muslim rhetoric such as in the 2016 American elections, media that depict Muslims as evil and oppressed like the film True Lies, and/or bills and policies that aim to restrict Muslim women like Bill 21 in Quebec, Canada. Islamophobic sentiments and actions often increase after events such as 9/11 when Muslims have to defend themselves to disassociate with allegations of terrorism and are directly affected by mass shootings like in Quebec, Canada, in 2017 and in Christchurch, New Zealand, in 2019. Muslims around the world suffer from Islamophobia be it through genocide, such as the Rohingya in Myanmar or the Uyghurs in China, or being restricted from practicing their religious beliefs like wearing the hijab or niqab in France.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".