Research Investigating Lexical Coverage and Lexical Profiling: What We Know, What We Don’t Know, and What Needs to be Examined
Bibliographic record
Abstract
Studies of lexical coverage are valuable because they reveal the importance of vocabulary knowledge to comprehension. Lexical profiling research is also extremely useful because it indicates the vocabulary knowledge necessary to understand different text types such as novels, newspapers, academic lectures, television programs, and movies. Moreover, lexical profiling research provides teachers and learners with concrete vocabulary learning targets that students can seek to achieve and evaluate their knowledge against. However, there are only three studies that have precisely investigated the effects of lexical coverage on reading comprehension (Hu & Nation, 2000; Laufer, 1989; Schmitt et al., 2011), two that have directly investigated its effects on listening comprehension (Bonk, 2000; Van Zeeland & Schmitt, 2013), and one that has done this for viewing comprehension (Durbahn et al., 2020). With few studies and few variables that may affect comprehension examined, discussions of the generalizability of lexical coverage findings are likely overstated. The aim of this article is to clarify earlier research findings and highlight areas where further research is needed.
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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 | 0.060 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.013 | 0.058 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".