Challenging lexical coverage conventions: Evaluating the vocabulary demands of family-genre film and television
Bibliographic record
Abstract
The contribution of studies investigating lexical coverage to the field of applied linguistics cannot be understated. Lexical coverage research has helped establish the vocabulary knowledge most essential for second language (L2) comprehension and elevate the importance of high-frequency vocabulary knowledge acquisition. Approaches to lexical coverage research have, however, begun to come under closer scrutiny in recent studies, with some experts questioning the accuracy of coverage estimates. Understanding these limitations, the current study applies an alternative approach to evaluating the lexical knowledge required to comprehend the OPUS-family-genre corpus, a collection of closed captions from 1597 family-genre films and television programs (10,744,767 tokens). In contrast to previous conventions that used band-based (1000-word) predictions of lexical coverage, in this study, coverage is evaluated at the individual word-unit level. It compares the coverage provided by four word lists: (1) a lemma list derived from tagging the OPUS-family-genre corpus, (2) a flemma list, and two word-family lists, (3) the BNC, and (4) the BNC/COCA. The study also models how a part-of-speech lexical tagger (TagAnt) can be used to evaluate lemma-based lexical coverage. The analysis revealed that English language learners will know 90, 95, and 98% of the running words appearing in family-genre films and television if they know the first 855, 2005, and 4393 flemmas, from the attached word lists. More simply, knowing the first 900 words from our supplementary word frequency lists would enable English language learners to start viewing family-genre films and television.
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 | 0.004 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".