A Concise Protocol for the Validation of Language ENvironment Analysis (LENA) Conversational Turn Counts in Vietnamese
Bibliographic record
Abstract
The aim of this study was to present a protocol for the validation of the Language ENvironment Analysis (LENA) System’s conversational turn count (CTC) for Vietnamese speakers. Ten families of children aged between 22 and 42 months, recruited near Ho Chi Minh City, participated in this project. Each child wore the LENA audio recorder for a full day. Two native speakers listened to 10-min extracts of the recordings from each family and labeled conversational turns according to the coding protocol. Their results were compared with the findings from the LENA software. A Spearman rank correlation test indicated a strong level of correlation between the LENA software and the human coders, r s (18) = .70, p < .001. The LENA System’s CTC provides a reasonably accurate estimate of conversational turns in Vietnamese recordings, showing that this protocol can yield significant results. Discrepancies between the coders and the software are discussed, and the strengths and weaknesses of the proposed protocol are highlighted.
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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 | 0.062 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".