Bibliographic record
Abstract
This essay interrogates the risks posed by artificial intelligence (AI) to intellectual labour and human skills and capacities. Employing insights from ethical philosophy, political theory, and the Marxist tradition in an engagement with recent manifestos calling for AI regulation, I adopt an interdisciplinary approach to the question of labour and technology, using translation work as a case study. Intentionally bracketing whether AI will be able to replace human translators, I explore the foundation of the conflict between AI and human intellectual work: namely that the former risks destroying the cultural practices and institutions that maintain the human ability to think and communicate in the most general sense. Even if regulation were to succeed in making AI more “ethical” – that is, more transparent, less exploitative, less biased, and less environmentally destructive – it would still be “unethical” in the strict etymological sense of the term that I advance here as a hermeneutic device: AI destroys the ethos (habits, abilities, way of being) of translation by degrading the cultural and institutional “training milieu” conducive to it. This conclusion is applicable to numerous domains of labour and has implications for education and democratic citizenship.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".