Using developmental data to explore frequency effects in production
Bibliographic record
Abstract
Low-frequency words are produced with longer duration and more extreme articulation than high-frequency words. It is unclear whether these differences are the product of online processes occurring during production, or instead result from the nature of stored exemplars. One way to disentangle these accounts is to consider changes over development. Online accounts predict that frequency effects arise due to the structure of the lexicon. Therefore, as the lexicon develops, productions should change as a function of individual words' frequencies. In contrast, exemplar accounts hypothesize that these effects occur because listeners are exposed to and store words with these acoustic properties; on these accounts, the effects should remain relatively stable across the lifespan. We asked children to name pictures that differed in frequency in both the child and adult lexicons. The pictures were in one of four categories: high frequency adult/high frequency children, low frequency adult/low frequency children, high frequency adult/low frequency children and low frequency adult/high frequency children. If children's production patterns reflect frequency in the adult lexicon, this would be evidence for an exemplar account of frequency effects; if, instead, children's patterns reflect frequency in the child lexicon, this would suggest that online processes underlie these effects.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".