Bibliographic record
Abstract
Fabián Severo’s collection of short stories, Viralata, from which this short story comes, was originally published in Portuñol, a “hybrid” mix of Spanish and Portuguese, as it is spoken near the city of Artigas in northern Uruguay. Portuñol, like other “hybrid” border varieties, has rarely been published, though it would seem that interest is growing since the 1990s, particularly in Uruguay. As a scholar of “hybrid”, diaspora, and transnational languages I decided to explore the possibility of translating this work into Spanglish, the “hybrid” mix of Spanish and English commonly heard among Latinxs in the US. Though the cultural realities of Portuñol speakers and Spanglish speakers is different, there are some important parallels: literature in both has emerged only relatively recently, little has been translated into either language variety, education is not conducted in either, and the dominant discourses around language in both contexts has traditionally favoured literature written in the prestige varieties of English, Spanish, or Portuguese—which should come as no surprise. Given this, I wondered about the experience, aesthetic, and cultural value of putting two distant borders of Spanish in contact through translation. This is my first translation of Fabián Severo’s work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".