Using the Language ENvironment Analysis (LENA) System to Investigate Cultural Differences in Conversational Turn Count
Bibliographic record
Abstract
Purpose: This study investigates how the variables of culture and hearing status might influence the amount of parent-child talk families engage in throughout an average day. Method: Seventeen Vietnamese and 8 Canadian families of children with hearing loss and 17 Vietnamese and 13 Canadian families with typically hearing children between the ages of 18 and 48 months old participated in this cross-comparison design study. Each child wore a Language ENvironment Analysis system digital language processor for 3 days. An automated vocal analysis then calculated an average conversational turn count (CTC) for each participant as the variable of investigation. The CTCs for the 4 groups were compared using a Kruskal-Wallis test and a set of planned pairwise comparisons. Results: The Canadian families participated in significantly more conversational turns than the Vietnamese families. No significant difference was found between the Vietnamese or the Canadian cohorts as a function of hearing status. Conclusions: Culture, but not hearing status, influences CTCs as derived by the Language ENvironment Analysis system. Clinicians should consider how cultural communication practices might influence their suggestions for language stimulation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".