Exploring the Vocabulary Makeup of Scripted and Unscripted Television Programs and Their Potential for Incidental Vocabulary Learning
Bibliographic record
Abstract
The present study investigated the lexical demands of scripted and unscripted television programs. To that end, two corpora consisting of 286 episodes from 14 different programs, both scripted and unscripted, were analyzed. The results indicated that the 1,000 most frequent word families, plus proper nouns, marginal words, transparent compounds, and acronyms, were required to reach 90% coverage in both scripted and unscripted programs. Furthermore, knowledge of the 2,000 most frequent word families accounted for 95% coverage in the unscripted programs, while, to reach the same threshold in the scripted programs, a vocabulary size of the 3,000 most frequent word families was needed. Regarding 98% coverage, vocabulary knowledge of 4,000 and 6,000 word families was required for the unscripted and scripted programs, respectively. A corpus-driven investigation was also conducted to explore the potential of both types of television programs for incidental vocabulary learning. Accordingly, the results showed that both types of programs may hold relatively great potential for learning words from the 2,000- to 3,000-word levels and might have some potential for the incidental learning of mid-frequency words (i.e., 4,000- to 9,000-word levels). Implications for using both types of television programs in language learning and teaching processes are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".