Transparency of Compounds and their Potential Importance in English Learner Reading Comprehension
Bibliographic record
Abstract
For adequate comprehension, second language (L2) learners need knowledge of 98% of the words in a text (Hu & Nation, 2000; Schmitt et al., 2011). However, vocabulary coverage studies used to support these findings have often excluded words such as proper nouns, ‘marginal’ words, acronyms/abbreviations, and compounds (e.g. ‘playground’). Compounds cover 1.2% of COCA corpus texts (on average), suggesting a significant role in reading comprehension (this study). Accordingly, a large ‘Yes/No’ test dataset (Brysbaert et al., 2021) was used to examine the degree to which 3345 compounds from Nation’s ‘Baseword 33 – transparent compound’ list are recognized by L2 English speakers as compared to their constituent stems (i.e., ‘play’ & ‘ground’) of each compound. Results revealed that compounds are recognized as English words far less than either stem constituent. Prototypical compound examples from the results are presented, and the implications for teaching and learning, and future research are also discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".