On the poetics and politics of the so-called ethnic detective novel
Bibliographic record
Abstract
In the very wide and diversified field of “murder fiction” (Rzepka 2005: 1), it would seem a relatively new and, therefore, polynomial and vague subgenre has slowly been taking shape, namely that of the “ethno-detective novels” (Erdmann 2009: 11) – also known as “multiethnic crime fiction” (Fisher-Hornung & Mueller 2003);“international crime fiction” (Krajenbrink & Quinn 2009); “‘minority’, ‘multicultural’,‘cross-cultural’ and ‘postcolonial’” or “‘ethnic’ detective fiction” (Matzke & Mühleisen 2006: 6-7). The burning question on the lips of the literary critic or scholar discussing this emerging genre is, as Matzke and Mühleisen so succinctly state, whether “practitioners of ‘ethnic’ detective fiction need to be ‘ethnic’ themselves in order to be ‘truly’ representative” (2006: 7). The answer to this question is conspicuous by its absence. Very few dare to enter such a debate as it often leads to politically and ethically sensitive issues, where there is a risk of essentializing categories of voice and experience by naturalizing non-mainstream voices or non-mainstream experiences and by ignoring the complex forces that produce them (Bergland 1994: 132). Nevertheless, in this paper I attempt to enter into that dreaded discussion by looking beyond the categories of ‘voice’ versus ‘experience’. Relying on Bart Keunen’s interpretation of the Bakhtinian chronotope, I compare two ethnic detective novels, where one is written by what Fischer-Hornung and Mueller refer to as a “cultural [outsider]” – The Beggar’s Opera (2012) by Canadian Peggy Blair – and the other is written “from an insider’s perspective” – Pasado perfecto (2000) by Cuban Leonardo Padura (2003: 13).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".