From the Ground Up: Tactical Mobilization of Grief in the Case of the Afzaal-Salman Family Killings
Bibliographic record
Abstract
Abstract This paper focuses on the murders of the Afzaal-Salman family in London, Ontario (Canada) in June, 2021. Immediately after the murders were reported, several hashtags on different social media emerged, focusing attention on the victims and commenting on the Islamophobia that resulted in their deaths. Through a critical discourse analysis of one of these hashtags, #OurLondonFamily that was used on Twitter and Instagram, this paper examines how grief becomes a conveyor of subjugated histories and experiences of Islamophobia, and a conduit through which the politics of identity surface. The paper argues that social media platforms like Twitter and Instagram allow for affective expressions of grief, demonstrating a networked sociality and articulating politics of opposition from the ground up. The affective publics that are engendered include activists and NGOs, whose social worthiness, high number of followers, coordinated and committed attention provides traction that allows for collective grieving that can be politically mobilized. As tactical trajectories, social media posts rupture the spectacle of a harmonious multicultural Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".