Examining the Trends of Islamophobia: Western Public Attitudes Since 9/11
Bibliographic record
Abstract
This article examined the trends of Islamophobia by looking at the changes in Western public attitudes towards Muslims and Islam that have occurred since 2001, in addition to the factors that have influenced such changes. It employed both qualitative and quantitative analysis in analyzing current public opinion poll data borrowed from: Angus Reid Global, the European Monitoring Centre on Racism and Xenophobia, the National Association of Muslim Police and the Arab American Institute. In addition, it analyzed current media reports that similarly illustrate Islamophobic trends after 9/11. Results indicate that the most dramatic increase in Islamophobic attitudes towards Muslims and Islamic institutions occurred in the UK immediately after 9/11, with a common theme exhibiting itself in comparing the various country reports and public opinion poll data examined in this study – namely, the involvement of factors such as Islamic clothing that commonly distinguishes Muslims from non-Muslims in inciting Islamophobia. In addition, results indicate that rather than decrease over time, as was initially hypothesized, Islamophobic attitudes are currently on the rise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".