Bibliographic record
Abstract
One of 2019’s most acclaimed documentaries, Waad Al-Kateab’s For Sama is an extraordinary feminist representation of the Syrian civil war (2011present). Al-Kateab impressively documents five years of the most traumatic contemporary conflict in the Middle East by focusing on personal confessions to Sama, her new-born daughter. Raw, dramatic, and sometimes unbearable to watch, it is a poetic tribute to a micro-level, “singularly unmanly”, and painfully intimate portrayal of war and hope (Montgomery). A mixture of love and horror unfold through a kaleidoscopic personal narrative that broaches macro-political and religious subjects without centralising them in the cinematic experience. This article discusses how Al-Kateab’s documentary is a novel and risky experiment that intermingles the female war gaze with a subtle, image-based Islamic feminism. Capitalising on Svetlana Alexievich’s “female war gaze”, which represents the invisible stories of women in war, I show how Al-Kateab’s cinematography expands the scope of the female war experience through carefully selected visual refences to Islamic ethical praxis, as interiorised by the camerawoman. For Sama is simultaneously an intimate motherly confession and act of both “listening” and “remembrance” (as the praxis of the Sufi Samāʿ suggests). In short, it mediates an ethical truth about the human condition in ruins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".