Introduction: Transnational Feminism in a Time of Digital Islamophobia
Bibliographic record
Abstract
In the nearly two decades following the events of 9/11, Western mainstream media have become obsessed with Islam, often sensationalizing Muslims as inherently violent, barbaric, and as undesirable Others. In the current technological era, the number of users who have taken to digital media and social networking sites (SNS) to express their anger, hatred, and even to make death threats towards Muslims has been increasing dramatically. Since before and after taking office, Donald Trump has done much to further exacerbate and justify the flames of these hateful pursuits, exemplifying the heightened state of anti-Muslim sentiment in the current digital landscape, in North America and beyond. In this interdisciplinary special issue of Islamophobia Studies Journal, we aim to a) document and make visible in the face of "fake news" and misinformation the various instances of ongoing and virulent Islamophobia and their different transnational itineraries and impacts, but also, and perhaps even more importantly, b) to document how such instances of hate and ignorance can be combatted through various modes of resistance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".