On Collaborative Translation and Subjectivity in the Humanities. A Conversation with <i>The Henri Meschonnic Reader</i>’s Translator Team1
Bibliographic record
Abstract
The publication of The Henri Meschonnic Reader finally offers English-speaking readers the opportunity to engage with Henri Meschonnic’s theoretical work in a coherent way. To realize this project, a team of six translator-scholars from the fields of linguistics, poetics, literary and translation studies worked together. The interview follows the Meschonnician idea that translating is an activity that transforms the translator’s subjectivity and examines its implications in the context of collaborative translation and (re)translation in the humanities. Looking back on their experience of working together, the translators reflect on how putting into practice the ethics of translation, defined by Meschonnic as “translating what the text does rather than what it says,” involves a conception of language as an activity, thereby challenging our understanding of the humanities and our systems of knowledge.
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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".