Mitigating Estrangement Through Autofiction: Domestic Discord, War, and Exile in the Works of Hanan Al-Shaykh
Bibliographic record
Abstract
This study portrays the theme of estrangement in the life and literary oeuvre of Hanan Al-Shaykh. It argues that her narrative technique of “autofiction,” a hybrid of autobiography and fiction, is a potent platform for resisting estrangement. Al-Shaykh's heroines paint a reflective canvas embodying the broader story that resonates with countless women experiencing alienation away from shattered roots and homelands. The research explores various estrangement facets, beginning with family alienation involving maternal abandonment and patriarchal coercion in Al-Shaykh's life. A second alienation arises from the destructive role of war in Lebanon, which not only crushes women in general but also marginalizes women writers from the canon of war literature despite their nuanced viewpoint on caregiving. The study then delves into a third estrangement caused by expatriation: the fate of millions of Lebanese women like Al-Shaykh, who live a diasporic existence struggling with a deep identity crisis, accentuated by cultural and linguistic disconnections in their foreign milieus, while traditional patriarchal expectations haunt them to their exile. This exploration culminates in reflecting on the reciprocal relationship between Al-Shaykh's life and her literary creation, as her heroines exemplify the capacity of autofiction to articulate universal estrangements rooted in personal torment.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".