Abject Talks Gibberish: “Translating” Abjection in Rabih Alameddine's An Unnecessary Woman
Bibliographic record
Abstract
The Lebanese Civil War, stretching over two decades of Lebanon's history, features prominently in any discussion of Rabih Alameddine's An Unnecessary Woman (2014), a novel fashioned according to the pent-up frustrations of a post-trauma period. Alameddine's novel manifests traumatic signposts of the civil war, which make it indelibly situational, and accordingly latches onto complex psychological issues. It is branded with the mark of “abject,” which besots its pages, a phenomenon that threatens identity beyond measure, triggering even an existentialist entropy. In making an effort to (persistently) “describe” this complex phenomenon beyond ken, the novel enmeshes in a baroque and a quite wordy style that tells of an arduous quest on the author's (and characters‘) part to find the “right” word for “abject.” Drawing mainly on Sigmund Freud's essay “The Uncanny” and Julia Kristeva's Powers of Horror, this article proposes to skirt the psychological archaeology of “abject” in An Unnecessary Woman. It argues that the Lebanese Civil War is not the originator of the characters’ feeling of abjection in the novel. Rather, it contends that this feeling, already inherent in the human being and thus universal, is activated by abject threats, such as, in this premise, the civil war, its suspect entourage, and aging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".