Circulation of Orhan Pamuk’s <i>Benim Adım Kırmızı</i> [<i>My Name is Red</i>] in contemporary Chinese-Indonesian literature
Bibliographic record
Abstract
This article is an exploration of contemporary Turkish and Chinese-Indonesian literatures with regards to a mid to late 18th Century literary niche: the it-narrative. Thinking ( noesis ) back and forth between centuries and different literary genres makes ( poiesis ) the conversation possible, which addresses the socio-literary imagination of the last four centuries. The authors re-examine the genre of it-narrative outside 18th Century studies and reassess the encounter of Turkish author Orhan Pamuk and Chinese-Indonesian author Alberta Natasia Adji within the socio-cultural and historico-political context of modern Turkey and Indonesia. The question is how Pamuk’s use of prosopopoeia in his 1998 novel Benim Adım Kırmızı ( My Name is Red ) influences Adji’s decision to use the 18th Century it-narratives in her 2019 short story I am Her Bracelet . Image by H005 from Wikimedia Commons: ‘Plates for sale on the Grand Bazaar (Kapalı Çarşı) in Istanbul’, CC BY-SA 3.0
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".