Canadian Muslim Excellence: A Time to Celebrate, Educate, and Reflect
Bibliographic record
Abstract
This podcast examines the increased incidents of Islamophobia in Canada and the impact of Islamophobia on Muslim Canadians. The overall project being presented seeks to bring attention to the need for intentional strategies embedded in education to help combat Islamophobia. A specific strategy of explicitly celebrating and highlighting Canadian Muslim Excellence is outlined. Sharing the stories of Muslim Canadians that have had a positive impact on our country, is intended to increase the positive messaging surrounding some of the wonderful contributions that Muslim Canadians have accomplished. This podcast also speaks to strategies in place to assist educators in continuing the work of dismantling the stereotypes and fear that come with Islamophobia. Providing the listener with resources to share the names of Muslim Canadians to be celebrated, accompanied by their memorable stories and newly developed lesson plans for the classroom, this project aims to increase the positive portrayal of Muslim Canadians and decrease the display of negative stereotypes and acts of hatred. Canada would not be the same if it were not for our diversity. We need to be intentional in celebrating, educating, and reflecting in ways that work to combat hatred.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.011 |
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".