Interpreting through AI: A note on the possibility of weaving ancient traditions with novel technologies
Bibliographic record
Abstract
Abstract Language, like any other semiotic code, is a sign system whose forms (words, phrases, sentences, etc.) are governed by the standing for principle (SFP). However, the SFP is more complex to envision when ‘composite forms’ such as novels and dialogues are involved. In this article, and following the work of Bakhtin and Gasset, we present the idea that a composite semiotic form, such as a paragraph in a novel, does not stand for a simple referential situation only, but provides us with a context for anticipation. We show how these ideas can be expressed in the state-of-the-art language models in AI (Artificial Intelligence) and the way AI may provide us with novel directions for developing old practices of interpretation.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.068 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".