Bibliographic record
Abstract
Taking the recent publication of The Gestural Origin of Language by David Armstrong and Sherman Wilcox as a starting point, this essay discusses a number of issues and difficulties raised by the idea that language first emerged as a gesture-language, only later to become spoken. It is argued that while modern sign languages may throw light on processes that are fundamental to language formation, they cannot be considered representations of an earlier form of language, as some writers seem to suppose, nor does their existence offer any support for a ‘gesture first’ theory. Rather, language must have been, from its first appearance, a multimodal phenomenon. It is pointed out that modern speakers, qua speakers speaking spontaneously, always employ several modalities together in a complex orchestration. However, the model of language generally followed in linguistics, whether the language studied is spoken or signed, does not usually take this into consideration. An abstracted idea of language is usually employed, developed largely because the systematic study of language usually considers language only in its written form, and not as it is manifested when spoken, when it is an activity that involves the whole body, and not just the so-called ‘speech apparatus’.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".