Framing the ‘White Widow’: Using intersectionality to uncover complex representations of female terrorism in news media
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
Following 21 September 2013, news media in the UK offered extensive and elaborate coverage of the Westgate Mall Massacre in Nairobi, Kenya. This act of terrorism, perpetrated by Al-Shabaab, left over 60 people dead. What news media considered particularly captivating was not the devastation of the attack, but the suspected involvement of Samantha Lewthwaite. She remained at the center of news media in Britain for several months after the attack, dubbed the ‘White Widow’. In this article, the authors employ an intersectional approach to explore the ways that race, religion, nationality, age, class, and gender converge in mediated representations of Lewthwaite. They argue that the application of intersectionality results in a more holistic understanding of the content and discursive impact of news narratives about female terrorists and find that news media both vilify and normalize Lewthwaite, representing her participation in terrorism through complex constellations of identity.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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