Natural Referent Vowel (NRV) framework: An emerging view of early phonetic development
Bibliographic record
Abstract
The aim of this paper is to provide an overview of an emerging new framework for understanding early phonetic development—the Natural Referent Vowel (NRV) framework. The initial support for this framework was the finding that directional asymmetries occur often in infant vowel discrimination. The asymmetries point to an underlying perceptual bias favoring vowels that fall closer to the periphery of the F1/F2 vowel space. In Polka and Bohn (2003) we reviewed the data on asymmetries in infant vowel perception and proposed that certain vowels act as natural referent vowels and play an important role in shaping vowel perception. In this paper we review findings from studies of infant and adult vowel perception that emerged since Polka and Bohn (2003) , from other labs and from our own work, and we formally introduce the NRV framework. We outline how this framework connects with linguistic typology and other models of speech perception and discuss the challenges and promise of NRV as a conceptual tool for advancing our understanding of phonetic development.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".