HONOURABLE MENTION: Appealing to Women’s Obligations to Join the Caliphate: Content Analysis of IS’ English Language Magazine Dabiq
Bibliographic record
Abstract
This paper qualitatively analyses the content if IS’ English magazine Dabiq to look for how they appeal to female recruits. The purpose is to illuminate if a relatively successful terrorist organisation appeals to women’s agency (manifestin as appeals to obligations) in their attempts to recruit them; proponents of feminist security studies would expect that they would use this tactic. To undergo this analysis, a coding scheme is created to determine whether the concept of obligation is being used in articles aimed at women, as well as a scheme to determine which theme of obligation is present, if any. The themes are Marriage, Children, Ummah, Khilafah, and Islam. Dabiq Issues 7 - 13 contain such articles, and therefore articles aimed at men and at a general audience are also analysed from each of these issues for comparative purposes. As follows feminist security studies, Dabiq contains appeals to agency in its articles for women, and these appeals to agency are often to obligations to themes beyond those reducing women to objects of male desire, such as political and religious ideology. Understanding what sort of messages IS uses in its attempts to recruit women can reduce the chance of researchers overlooking certain Islamic terrorist propaganda efforts, and also has important policy implications for counter-terrorism.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".