Bibliographic record
Abstract
Among the most intriguing aspects of human language is its sound system and how the human infant comes to acquire it. Three major questions are addressed in this chapter. First, are there any similarities to be found between features of animal vocalizations and those of early infant vocalizations? In other words, can possible phylogenetic origins of human infant vocalizations be found in the vocalizations of nonhuman primates? If so, where exactly is the overlap? Alternatively, are even the earliest stages of human infant vocalizations quite distinct from animal vocalizations? Second, what are the major developments in the early ontogeny of the human vocalization system, and do they vary across infants with different characteristics or backgrounds? The focus here is on such features of vocal development as the onset of canonical (reduplicated) babbling, phonetic preferences, consonantal repertoire, consonantality (degree of consonant use), and complexity (combination of different consonants). Third, do variations in features of babbling across infants make a difference in language acquisition? Is just babbling itself an important precursor to language, or is the quality of babbling also important? In this chapter, infant prelinguistic vocalizations are considered to be all phonated sounds (with vibration of the vocal cords) that are audible and are not crying, fussing, laughing recognizable words, imitated animal sounds, or imitated conventionalized expressions ( uh-oh ). These criteria are consistent with those used in most research on early infant vocalizations except that some researchers exclude also grunts (for example, Oller & Lynch, 1992) and some include words (for example, Vihman & Greenlee, 1987).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".