Factors that Contribute to the Development of Receptive and Expressive Comprehension of Figurative Language in English Language Learners (ELLs): A Systematic Scoping Review
Bibliographic record
Abstract
The objective of this study was to conduct a systematic scoping review of published studies on factors that contribute to the development of figurative language (FL) in English Language Learners (ELLs). To date, research on FL processing in ELLs has not been reviewed systematically, and a more nuanced understanding of the nature of possible difficulties that ELLs face is limited. A total of 89 sources were identified through electronic database searches and handsearching in peered-reviewed journals. After screening those studies, 18 papers were included for data extraction, and narrative synthesis. Findings indicated that (a) few studies have examined FL in ELLs, and they were not consistent in terms of their focus, methodology, participants and results, (c) studies on FL in ELLs focus only on one factor, and (d) FL development in ELLs has primarily been examined from a pedagogical and teaching perspective rather than a systematic psycholinguistic and developmental one.
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 | 0.021 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".