2: Crossing Borders, Crossing Disciplines: Ali and Nino in the Twenty-First Century
Bibliographic record
Abstract
LEV NUSSIMBAUM—alias Essad Bey, alias Kurban Said—situated the eponymous hero and heroine of his 1937 novel, Ali and Nino , at linguistic, geographic, national, religious, and cultural crossroads. The love story begins in the city of Baku in the Caucasus, a region with a checkerboard of over fifty ethnicities, among them Armenians, Azeri, Daghestani, Georgians, Persians, Russians, and Turks. The Caucasus region is geographically situated along the historical borders between Europe and Asia, between Christianity and Islam, and between the Shi'ite and Sunni branches of Islam. Most of the action takes place in Azerbaijan with one significant sojourn into Persia, which in the imaginary space of the novel becomes a secluded haven isolated from political strife. In an attempt to understand themselves, each other, and the intersecting positions they occupy, the Muslim Ali and Christian Nino must respond to and assess their constantly shifting and opposing environments against the backdrop of the First World War and the Russian Revolution. They seek and briefly find a middle ground, a utopian space of understanding and acceptance that historical reality renders impossible. One's understanding of the characters becomes more entangled still upon realization that their story was penned in German by a Jewish Azeri, Lev Nussimbaum, who converted to Islam, published under two pseudonyms, and died in Italy while fleeing the Nazis. Such complexity defies attempts to settle on a definitive interpretation of the text while demanding exchange as a prerequisite to deeper understanding: exchange between the text, its readers, and a wide range of other sources. This insistence makes Ali and Nino an ideal text for teaching textual analysis as a hermeneutic process, and for helping students develop interpretive and research skills. As authors of this article, we come to our subject from different disciplinary (German studies and Near East studies), national (American and Iranian-Canadian), and religious (Christian and Muslim) backgrounds. Our discussions have led to our use of the novel in three very different classroom settings (a senior capstone course, taught in German, on encounters between East and West, and two junior-level courses, taught in English, on women and Islam and on women in Near East history) at two different universities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".