Algorithmic Mimesis: Translation, Technology, Resistance
Bibliographic record
Abstract
Translation technologies often figure translation as a simple process of linguistic transfer from one code to another or as a question of selecting the correct matching segments from a database. The prominence of such technologies in the digital age has thus renewed discussions of fidelity and equivalence for translators. The critical attention given to broader cultural and textual contexts that came into focus with the cultural turn seems at risk of disappearing into cyberspace. However, the ongoing proliferation of textual production and reproduction also foregrounds the possibilities of variability and difference in repetition. Using the foibles of technology as catalysts for their own creative ventures, digital-age artists such as Urayoán Noel and Malinda Kathleen Reese channel deficiencies productively in their art, revealing the unsuspected potentials of digital technologies. Such a view of translation as creation challenges the commonplace notion that translation is a scientific act of “carrying across,” a purely semantic transfer that results in the (illusion of) identicality of source and target. Echoing Lévi-Strauss’s notion of “bricolage”—the means by which people retrieve and recombine cultural materials to create new content—Reese and Noel shatter the semantic shackles of identicality by using technology to retrieve and transform the material scraps of language and culture. Their art helps us reconceptualize translation and go beyond fixed notions of what a translation should be or do in terms of fidelity and equivalence. Their playful misuse of machine translation and voice-recognition software allows for a critical analysis of the tension between the universal and the particular as it relates to the act of translation, and does so in a way that uses formal experimentation and humour to resist traditional power dynamics.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".