The Culture of Exile and Narratives of Resistance: A Study of Munif’s Cities of Salt and Naipaul’s the Middle Passage
Bibliographic record
Abstract
Fictional texts still remain a forceful medium in understanding the turbulent global culture at the end of the millennium. The language of literature is greatly affected by the struggle between two mutually opposed forces: the oppressor and the resisting power of the oppressed. The oppressed and the exploited of the earth maintain their defiance: liberty, through resistance. The biggest weapon against this defiance is to annihilate their belief in their past, roots, culture, or even their names and ultimately in themselves. It makes them want to identify with that which is remote; for example other people’s language. Ideas are implanted that any possibility of success or triumph is a remote ridiculous dream. This in turn creates a collective despair and a wasteland where the oppressor presents himself as the cure. Remembering, looking back in anger or even imagining are all acts of resistance and of lending coherence and integrity to a history and a homeland interrupted, divided or compromised by instances of loss. Redressing forcibly forgotten experiences, allows the silences of history to come into word, and makes us imagine alternative scripts of the past, hence invariably changes our understanding of the present. The present paper aims at investigating narratives that recuperate losses incurred in migration, exile and dislocation. Forced or voluntary immigration is discussed as part and parcel in the narratives that originate at border crossings and that cannot be bound by national borders, languages or traditions through Naipaul’s The Middle Passage, and Munif’s Cities of Salt, where the chaotic dynamics of a world constantly on the move creates resistance and possibly confrontations, mirroring the fragmented consciousness of postmodern culture elucidated by HomiBhabha and Frantz Fanon.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.032 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".