Bibliographic record
Abstract
This article begins with a critique of the language theory of cognitive scientists such as Steven Pinker (The Language Instinct), who describe in grammatical terms the complexity of human language. Their account of the pragmatics of language, however, is too simplistic, with Pinker seen as an idealist, in part because he imagines the context of speech only as shared information, neglecting the complexity represented by the conditions of utterance and seeing language as data to be processed between two bodiless computing machines. Bakhtin’s different positions on language are then examined. For him, people speak with their bodies, not only their brains. Bakhtin, unlike Pinker or Saussure, did not believe that we have dictionaries in our heads, which we consult at will. For Bakhtin, the experience of language consists not of a series of positions taken, but a series of failed attempts to find a position, because there is no position available in which to respond to the demands made on us. In underlining the alienness of discourse and language, Bakhtin is a realist and provides a useful counterpoint to the idealistic and naïve positions held by some cognitive scientists.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".