The Romani Ethos: A Transnational Approach to Romani Literature
Bibliographic record
Abstract
In the context of the sociopolitical articulation of the Romani diaspora, this paper explores how its narrative is supported in four literary works written in different languages and national settings – Fires in the Dark by Louise Doughty, Camelamos Naquerar (We want to speak) by José Heredia Maya, Goddamn Gypsy by Ronald Lee, and Dites-le avec des pleurs (Say it with tears) by Mateo Maximoff – shaping a transnational/diasporicliterary production. Departing from the existence of a common Romani ethos, the analysis focuses on how these literary works shape a transnational/diasporic literature by representing the specificities of the Romani history – in particular the recollection of traumatic collective experiences – through a number of narrative strategies, such as self-representation or the depiction of cultural memory.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".