Bibliographic record
Abstract
In this article, I address the inadequacies in how we currently conceptualize spaces for dialogue and debate around issues involving race and religion. Even in a climate where many organizations now acknowledge equity, diversity, and inclusion requirements, there are still numerous challenges, particularly for racialized individuals, including those who may experience overlapping forms of oppression. Drawing on concepts such as intersectionality, muted group theory, and the public sphere, I suggest that many existing channels and approaches are especially inadequate for academics and activists who are racialized or belong to religions that are marginalized in Western societies, such as Islam. These avenues do not allow for an articulation of the complex, sometimes contradictory realities lived by these individuals, where choosing a seemingly progressive side consistently and publicly may mean disowning or disadvantaging one’s own family or community members. Ultimately, I argue both that we must reconsider the potential for education and dialogue enabled by seemingly one-way platforms, such as film and television, and that the platform is less important than the approach we bring to using it, since increasingly we must prioritize windows for empathy within any mediated spaces we employ for learning or dialogue.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".