The Vocabulary Richness of Children’s Television in Ireland: A Cross-generational Comparison
Bibliographic record
Abstract
This research investigates whether the vocabulary richness of children’s programming has changed over one generation, and therefore compares the programmes offered in 1992 to those offered in 2017. Three hours of programming were sourced, transcribed and coded using the Computerised Language Analysis (CLAN) software (MacWhinney, 1984). CLAN is a language analysis software originally developed for the purpose of analysing child language which allows for the detailed transcription and analysis of linguistic data, including statistical measures of lexical diversity (Pye & MacWhinney, 1994). The total words, words per minute, vocabulary diversity, total object, action, attribute and affective-state words as well as the total object, action, attribute and affective-state words spoken in the presence of a referent were calculated and compared for the programming. The vocabulary richness of children’s television has decreased over time. The number of words spoken in the presence of referents in the programming has increased over time, with this increase being significant for action and attribute words. This pattern of findings reflects a trend in children’s television towards the production of programmes of reduced lexical complexity which may facilitate children’s word learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".