Investigating the Lexical Demands of English-as-an-Additional-Language and General-Audience Podcasts and Their Potential for Incidental Vocabulary Learning
Bibliographic record
Abstract
This study investigated the lexical demands of English-as-an-additional-language (EAL) and general-audience podcasts and their potential for incidental vocabulary learning. Two corpora (i.e., one EAL and one general-audience) comprising 1,188,512 tokens were analyzed to determine the necessary vocabulary knowledge to reach 90% and 95% coverage. The results indicated that 2,000 and 3,000 word families plus proper nouns (PN), marginal words (MW), transparent compounds (TC), and acronyms (AC) covered 90% and 95% of words in podcasts, respectively. The results also showed that EAL and general-audience podcasts required 1,000 and 2,000 words families to reach 90% coverage, respectively. Regarding 95% coverage, knowledge of 2,000 (EAL) and 3,000 (general-audience) word families was required. The results also demonstrated almost 60% of word families from the 2,000-word level were encountered 15+ times in each corpus, suggesting podcasts may hold relatively great potential for learning such words incidentally. Furthermore, our findings indicated that there was some potential for incidentally learning words from the 3,000-word level in both corpora, while general-audience podcasts may hold greater potential in this regard. Implications for using podcasts in language pedagogy are also 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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".