Sending balms across geographies of grief: Transoceanic letters on Muslim aliveness
Bibliographic record
Abstract
We are racialised non-Black Muslim women who are part of South Asian (Indian) and Palestinian diasporic communities living in so-called Canada and so-called Australia, respectively. We met at the first virtual gathering of the Afro Diaspora Futures in Education Collective. Through our conversations at this gathering, we learned about each other’s work with and for Muslim communities in our respective locales, where Islamophobia has become increasingly deadly. For both of us, our encounter was unexpected and welcomed. At this first gathering, Kevin Quashie (Citation2022) invited us into encounters with slowness as a poetics of Black method. Thinking through slow methods with Quashie, alongside deadly world-altering attacks on Muslim communities, creates spaces for us to grieve, offer and receive care, and affirm life. In this engagement with Black and Indigenous feminist epistolary methodologies, we discuss what it means to be studying and teaching in settler universities, where we have witnessed and experienced a lack of care that renders our greivability as Muslims questionable. We are heeding Quashie’s call; we are slowing down time to think and write together with Black aliveness toward Muslim aliveness, to send letters as balms across geographies of grief.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".