Chomsky on the Creative Aspect of Language Use and Its Implications for Lexical Semantic Studies
Bibliographic record
Abstract
Observations on the creative aspect of language use constrain theories of language in much the way as those on the poverty of stimulus do. The observations as Chomsky discusses them will be explained and their consequences for a semantic theory of the lexicon explored. Introduction Chomsky began to mention the creative aspect of language use in the early 1960s; his 1964 Current Issues discusses it. In 1966, Cartesian Linguistics takes it up in detail. At the end of the 1950s he had read extensively in the works of Descartes, Cudworth, Humboldt, and others in the seventeenth to mid-nineteenth centuries “Cartesian linguistics” tradition, and they provided a framework for articulating these ideas. Arguably, though, they were implicit in his review of Skinner and even in The Logical Structure of Linguistic Theory (ca. 1955). The creative aspect observations, along with the poverty of stimulus observations, offer a set of facts with which his and – he holds – any science of language must contend. However, he thinks that the lessons of the creative aspect observations in particular are often ignored, especially in dealing with semantic issues such as truth and reference, meaning and content. He may be right. I review the creativity observations and discuss some of their implications and suggestions for a semantic theory of the lexicon. In their light, I discuss briefly James Pustejovsky's different approach.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".