The biology of language and the epigenesis of recursive embedding
Bibliographic record
Abstract
Theorists have oversold the usefulness of predicate logic and generative grammar to the study of language origins. They have searched for models that correspond to semantic properties, such as truth, when what is needed is an empirically testable model of evolution. Such a model is required if we are to explain the origins of linguistic properties by appealing to general properties of linguistic engendering, rather than to the advent of genotypes with the propensity to produce certain brain mechanisms. While the latter sort of explanation has a place, no theory can be considered an ‘evolutionary’ theory without the former. We introduce a general notion of engendering, whose primary virtue is its freedom from assumptions regarding the nature of colloquial change. We use it to frame a conjecture about the evolution of centre embedded clauses; one which makes the fewest possible assumptions about the neural requirements upon individual brains. Keywords: biology of language; epigenesis; engendering; evolution; mutation; population; recursion
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".