How to End a War: Remnants of Hope and Terror in Danny Ramadan’s The Foghorn Echoes
Bibliographic record
Abstract
In the novel, The Foghorn Echoes (2022) by Danny Ramadan, readers are introduced to two young men, Hussam and Wassim, who love each other but whose lives are forever changed by a terrible event. Though this event marks the beginning of their end, they are met with several encounters that continue to separate them, as they grapple with what it means to be queer in Syria and what it means to be refugees elsewhere. Both their stories, told back and forth between the two young men, reveal the cruel optimism that is situated in the relationship between the good life and the queer struggle of romantic life. In other words, their desire for a better life as queer refugees becomes cruel when it becomes an obstacle in and of itself. For Hussam, readers witness this devastating blow as he is haunted by the death of his father and then by his separation from Wassim, as he struggles to build a better life in the nation-state of Canada. Wassim, on the other hand, has become a refugee in his own homeland, in this case, Syria during the Civil War, and he comes to view himself as a problematic object. Through both of their lives, it is revealed that the reality of queer Syrian refugees is inseparable from the complicated and oppressive histories that mark them such as the war and their forbidden love, whether they remain in the homeland or seek to build a good life somewhere else.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".