Pronounceability: A Measure of Language Samples Based on Children's Mastery of the Phonemes Employed in Them
Bibliographic record
Abstract
56 samples (n > half a million phonemes) of names (e.g., men's, women's jets'), song lyrics (e.g., Paul Simon's, rap, Beatles'), poems (frequently anthologized English poems), and children's materials (books directed at children ages 3-10 years) were used to study a proposed new measure of English language samples--Pronounceability-based on children's mastery of some phonemes in advance of others. This measure was provisionally equated with greater "youthfulness" and "playfulness" in language samples and with less "maturity." Findings include the facts that women's names were less pronounceable than men's and that poetry was less pronounceable than song lyrics or children's materials. In a supplementary study, 13 university student volunteers' assessments of the youth of randomly constructed names was linearly related to how pronounceable each name was (eta = .8), providing construct validity for the interpretation of Pronounceability as a measure of Youthfulness.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 0.000 |
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".