Historical, Darwinian, and current perspectives on the origin(s) of language
Bibliographic record
Abstract
Language has universally been felt to constitute the attribute that sets humans apart from other species. The quest to understand why and how the capacity to speak came to be has been central to our understanding of the nature of humankind. In this chapter, we focus on the speculations and explanations of the origin and evolution of language, highlighting attempts to answer this question from the ancient Greeks to the present day. A number of perspectives are presented, more or less in phase with the chronological development of the related ideas. Early thoughts on language origin in antiquity, the language deprivation experiments, and the Christian-era reflections on the origin of language constitute the first perspectives. Next, some pre-Darwinian accounts of the origin of language are explored, specifically Condillac’s and Monboddo’s views, as best reflecting the spirit of the Enlightenment, and Herder’s treatise on the subject. The historical context of the Société de Linguistique de Paris’s ban on this very topic is then examined, revealing the enduring misperception of its nature and causes. Charles Darwin’s views, seldom part of discussions on the origin of language, are also presented. We close the chapter with a brief overview of current questions and directions that characterize contemporary efforts at elucidating this fascinating problem.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".