Bibliographic record
Abstract
A recurrent concern of Daniel Simeoni’s writings is the concealed or disguised cultural origins of theoretical reflection or absence of reflection on translation. Allying this concern to a discomfort around particular kinds of universalist claims, this article examines forms of translation thought and practice that have emerged in the digital age. Two approaches to thinking about translation, “massive” thinking, and “detailed” thinking are used to situate particular kinds of translation practice in the era of automation and semi-automation. The strategic importance of detail in translation practice is located within the rising popularity of gist or indicative translation. Underlying both the “massive” and “detailed” approaches to translation, it is argued, are two different approaches to the question of the universal. The tension between easy universalism and difficult universalism is seen as bound up with projections of power and influence from which, as Simeoni repeatedly argued, translation and thinking about translation are not immune. In order to further develop the implications of difficult universalism for translation thinking and practice, the notion of “gap” is opposed to that of difference. The idea of “gap” avoids the reifying thrust of typicality that often underlies the invocation of difference and favours not so much the celebration of identity as the cultivation of fecundity. In this view, translators look to languages and cultures not so much for values as for resources. Where these “gaps” might be located is, of course, a source of endless conjecture but it is argued that in translation practices in the digital age, one place to look is in the debates around quality and what quality might mean in a digital age. The challenge quality poses for extensive universality is framed within Simeoni’s notion of the translator as borderline agent.
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 | 0.029 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.066 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".