On the relevance of pidgins and creoles in the debate on the origins of language
Bibliographic record
Abstract
In the literature on the origins and the evolution of language, the general assumption is that language started as a restricted code, referred to as “protolanguage.” Since there is no direct access to data manifesting the nature of incipient human language, it is inferred that restricted linguistic codes that are presently available may provide a window on the nature of protolanguage. Pidgin languages feature among the restricted codes that have been identified in the literature (e.g. Bickerton 1984). This chapter bears on the relevance of pidgins (and creoles) (PCs) in the debate on the origins and evolution of language. The first part is dedicated to the Bickertonian approach to pidgin and creole genesis. It presents the main features of this research paradigm, as well as a critical discussion of its various components. The second part reports on the shift in research paradigm in the field of pidgin and creole studies, from the study of language varieties to the study of the processes that yield these language varieties. On the basis of the data and analyses presented in the first two parts of the paper, the third part addresses the question of whether the pidgin/creole sequence actually does provide us with a window on the protolanguage/language sequence. My conclusion is twofold: First, pidgins and protolanguage are not alike. Second, the sequence pidgin/creole does not give us a window on the protolanguage/language sequence. Major arguments include the fact that pidgins, even restricted ones, are too elaborate to serve as analogues of protolanguage, and the fact that PCs are not created ex nihilo .
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".