Introduction: Transnational Feminism in a Time of Digital Islamophobia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it