Bibliographic record
Abstract
Islamophobia, as a global phenomenon, has seen significant impacts in various countries, including Canada. This research aims to investigate the dynamics of Islamophobia in Canada, explore the factors that influence public perceptions, and analyse its impact on Muslims. Using media content analysis, community surveys, and case study methods, this research identifies a number of cases of discrimination, negative rhetoric, and violence faced by Muslim communities in Canada. The study also examines how the mass media plays a key role in shaping public perceptions of Islam, as well as the extent to which government policies and regulations reflect the protection of Muslim rights. Findings highlight trends of inequality in everyday life, including in the workplace, education, and social interactions. In addition, the research discusses the efforts that have been taken by the government, Muslim community organisations to address Islamophobia. In particular, focus is given to educational initiatives and interfaith dialogue as a way to promote mutual respect in Canada's multicultural society. The study concludes by highlighting the challenges still faced by the Muslim community in Canada and emphasising the importance of cross-sectoral cooperation to create an inclusive and equitable environment for all its citizens, regardless of religious or cultural identity.
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 | 0.000 | 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 | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".