Bibliographic record
Abstract
English Abstract: Fiction might not be formally the same genre as ethnography, but fiction remains a legitimate companion to anthropological reflection. Placing Kafka alongside the authors of this special section of SA/AS allows me to read them just a touch beyond their central positions: especially for what they can teach us about untranslatability, ‘impass-ability’ and impossibility, as a vital part of ethnography and of relating more generally. Aft er discussing the texts of this section, I address questions posed to our panel at EASA2020. I then discuss what I call Brazil’s most untranslatable novel, which sheds unique light on contemporary anthropological worries about untranslatability, or impossible-to-fully-bridge difference. French Abstract: La fiction n’est peut-être pas formellement le même genre que l’ethnographie. En tant que produit d’une « observation sérieuse » quotidienne (Wood 2014[2020]) par contre, la fiction reste un compagnon légitime à la réflexion anthropologique. En plaçant Kafka aux côtés des auteurs de cette section spéciale de SA/AS, je peux les lire un peu au-delà de leurs positions centrales, en particulier pour ce qu’ils peuvent nous apprendre sur l’intraduisibilité, l’«impasse-abilité» et l’impossibilité, parties essentielles pour l’ethnographie et des relations/rapports plus généralement. Après avoir discuté les textes de cette section, je réponds aux questions posées à notre panel lors de l’EASA2020. Je discute ensuite de ce que j’appelle le roman le plus intraduisible du Brésil, qui jette une lumière unique sur les préoccupations anthropologiques contemporaines concernant l’intraduisibilité, ou la différence qui est impossible à combler entièrement.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".