How can Linguistic Meaning be Grounded – in a Deconstructionist Semiotics?
Bibliographic record
Abstract
Deconstruction is one of the more (in)famous theories in recent times. In this paper, I argue that the theory of deconstruction, proposed by Derrida in particular, should be read as a systematic and rigorous examination of key philosophical and semiotic notions, such as sign and meaning. The relevance of taking deconstructive critique seriously is explored with the point of departure in Derrida’s argument that linguistic signs are characterized by repeatability. This view is situated against attempts to ground language in context, speaker intentions and truth conditions, showing how deconstruction challenges these attempts for not taking the repeatability of signs sufficiently into account. Instead, deconstructive semiotics radicalizes the idea that linguistic signs always involve differential structures that postpone the determination of meaning. While this might be read as a skeptical conclusion, I propose that it should be positively interpreted as a relevant contribution for the theoretical understanding of language, signs and meaning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".