The Translator’s Art of Failure: Engaging the Other in Imperfect Harmony
Bibliographic record
Abstract
Rather than view translation as a compromise at best, a failure at worst, and always derivative and secondary, I explore the practice of literary translation as one of engagement: with the text, with the Other, and with the world. I will discuss the nature of this engagement as dynamic, electric, life-affirming, an encounter and a dialogue that offers an opportunity for both unification and separateness, freedom and intimacy. From this perspective of the personal, I will reach into the realm of the “political,” the broader context within which translators work—the power differentials, the objective material conditions—and the specifics of my own translating relationship: between Latin American Spanish and North American English. My understanding of that relationship—which could be framed as the theory behind my practice—influences what I want to translate—passionate engagement—and how I translate: how I approach the object of desire. I will read from and discuss several of my most recent translations, texts that in themselves explore questions of language and power: the power of language to subvert, and the dis-harmonious and unequal association of languages and cultures in this “globalized” world, where issues of separateness and unification are often a matter of survival.
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 | 0.037 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.083 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".