Bibliographic record
Abstract
Abstract It may appear counterintuitive to suggest a connection between language evolution and linguistic realism. Only biological objects evolve but linguistic realism holds that natural languages are abstract objects. However, given the fact that currently no approach to language evolution can account satisfactorily for all aspects of language, I suggest that reconsidering the ontological status of natural languages might lead to novel approaches to language evolution puzzles. Most contemporary work on language evolution assumes without argument that natural languages are either biological entities or produced by biological organs (human brains), and focuses on brain evolution, language acquisition, and communication systems of other primates. Yet, so far such approaches have been unable to account for some aspects of grammar. Furthermore, to date little is known about the bio-physiological implementation of natural languages. I suggest that the debate could profit from paying closer attention to the ontological status of language and the exact relationship between language and biology. Finally, I discuss the kinds of evidence used in linguistic research and demonstrate that, contra to widespread belief, the linguistic Platonist is neither relying on inferior evidence nor ruling out evidence that is clearly relevant to linguistic research.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".