Combatting Islamophobia: Addressing an Ongoing Threat to Building the Society We Deserve
Bibliographic record
Abstract
rowing up in Ottawa as a child and then as a teenager, the idea that I could ever one day be discriminated against because of my faith was not something I had ever worried about or imagined.Not when I sat with my mother on the second floor of Ottawa's first ever mosque on Scott Street, nestled in between other women and their children waiting for Friday prayers to begin.Not when I was invited to talk about Islam with my fellow fourth graders at Orleans Wood Elementary School by a school principal who taught me that our differences were to be shared and celebrated.Not when the city's Muslims began overflowing the small makeshift mosques that started popping up across the city, and then needed to eventually rent larger and larger spaces for Eid prayers-Lansdowne Park, Tudor Hall, community centres, school gyms-to accommodate the multicultural masses of men, women, and children in colourful traditional dress, balloons and sticky desserts clutched by small hands.In fact, it was actually my father who worried when I told him that I had decided to don the hijab before my final year of j-school.9/11 hadn't happened yet, but still, my dad feared that such an obvious marker of difference would be a barrier to succeeding in Canada.He had left Egypt in the late 1970s, and while he would have a very successful career working at Transport Canada spanning nearly three decades, he had seen discrimination and knew that it could hold me back.Yet, as I have shared elsewhere, I had drunk the uniquely Canadian multicultural Kool-Aid, telling him confidently-as only daughters can-that if I couldn't choose to wear a hijab and succeed in a place like Canada, then where could I?
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.030 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".