Post-traumatic stress disorder (PTSD) in Zoulfa Katouh’s As Long as the Lemon Trees Grow
Bibliographic record
Abstract
This paper aims to analyze the representation of Post-traumatic Stress Disorder (PTSD) in Zoulfa Katouh's novel, As Long as the Lemon Trees Grow (2022). Katouh, a Canadian-Syrian author who specializes in drug sciences, sets her debut novel against the backdrop of the war in Syria in the aftermath of the Arab Spring. The protagonist, Salama, a teenage girl pursuing an undergraduate degree in pharmacy, faces the harrowing realities of war and volunteers as a surgical assistant amidst the chaos of bombings and sniper attacks. As a result of the war, Salama tragically loses almost all her family members, and in response to her fear and anxiety, creates a hallucinatory male figure named Khawf. Khawf, which is the Arabic word for fear, serves as a manifestation of Salama's PTSD and a symbolic representation of her traumatic experience, blurring the lines between reality and imagination and highlighting the psychological toll of living in a war-torn zone. Hence, this paper explores the nature of PTSD, as depicted in the novel, and examines how Salama's sense of responsibility toward injured civilians and her feelings of guilt toward those she could not save influence her psyche, leading her to avoid and repress memories, unleashing her hallucinations and defense mechanisms. The theoretical framework of this study is primarily shaped by Anke Ehlers's research on mental defeat and alienation in victims of political trauma, Horowitz's stress response theory, and Stanley Lyndon and Philip Corlett's exploration of hallucinations as perceptual disturbances in cases of PTSD. Finally, this paper aims to present a deeper understanding of the psychological trauma inflicted by war and the complexities of human defense mechanisms in the face of adversity by analyzing Katouh's portrayal of PTSD symptoms that Salama, the protagonist, suffers immensely.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".