Digital empowering positivity: Syrian women's counter-discourse for resistance and healing
Bibliographic record
Abstract
In recent decades, global development agencies and popular media have co-opted the language of women's empowerment. This article explores how displaced Syrian women from the Global South negotiate and reclaim empowerment through digital media practices in their everyday lives. Based on fieldwork conducted between 2018 and 2021, it draws on 23 interviews with women displaced by the 2011 Syrian uprising-turned-war. The analysis uses a Cultural Discourse Studies (CDS) framework, which emphasizes the agency of marginalized communities in crafting empowering discourses, alongside thematic analysis of participants' perspectives on empowerment, agency, and representation. Findings reveal how Syrian women exercise agency by creating safer, women-only digital spaces that respond to social, political, and material constraints. Within these spaces, they co-construct a counter-discourse I term ‘digital empowering positivity’. This became a survival strategy against the negativity engendered by intersecting oppressions – war, displacement, racism, misogyny, and other forms of marginalization. These discursive practices fostered collective healing and solidarity, resisting exclusionary voices from dominant groups. This study amplifies the voices and experiences of displaced women from the Global South, highlighting their use of digital media to challenge power structures and represent their own values and interests.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| 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".