Bibliographic record
Abstract
This essay consists of a meditation upon the emotions, affects and ethical compromises which surround the translation of texts as complex and delicate as Bharati Mukherjee’s short story “The Management of Grief”. This well-known piece of fiction offers a very painful account of how the families of the victims of the Air India Flight 182 attack in 1985 managed to survive the enormous grief of losing their loved ones in such abrupt, violent and unjust manner. The essay author, who decided to translate this story into Spanish so that it could be enjoyed by a wider readership, shares her thoughts regarding the demands of such painful yet necessary task. The whole area of Postcolonial Studies, where she develops her scholarly career, is unfortunately rife with such testing moments, and she wonders about the convenience or even the pertinence of such scholarly/textual interventions.
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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.017 |
| Insufficient payload (model declined to judge) | 0.028 | 0.021 |
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".