Dumb intelligence? Translation as technological mediation
Bibliographic record
Abstract
We propose a semiotic approach to understanding and assessing language technologies. Specifically, by adopting a recent semiotic and broad concept of translation, developed by Kobus Marais, we bring semiotic theory into the service of philosophy of technology. Our perspective reveals that commonly assumed expectations about language generative technologies are mistaken and misleading when shaped through an ideal of engineering humanlike interlocutors, which we illustrate with examples. We find that (software) engineering pursues this ideal, which, fuelled by classical humanism, assumes that language is an anthropic marker. By explaining (technological) emergence as a semiosic process, we develop a robust underpinning for the Mind–Technology Thesis, namely refuting mind-and-matter substance dualism through an evolutionist perspective that construes technology as mind-work. In this vein, semiotics corroborates with externalist theories of mind and postphenomenology in understanding mind and technology as mutually intrinsic. This leads to a semiotics-grounded advocacy of the view in philosophy of technology, championed by Elena Esposito, that for artefacts properly to communicate with biological organisms, they do not require “intelligence”.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".