English Language Learners’ Comprehension of Logical Relationships in Expository Texts: Evidence for the Confluence of General Vocabulary and Text‐Connecting Functions
Bibliographic record
Abstract
Abstract Conjunctions facilitate text cohesion and comprehension by making explicit the logical relationships between ideas in written language. Conjunctions may be challenging for English language learners (ELLs) because of their novel, abstract, and text‐connecting role. In this longitudinal study we aimed to clarify the connections among comprehension of logical relationships, general vocabulary knowledge, and reading comprehension in elementary school‐aged ELLs. We assessed these skills—along with decoding, working memory, and nonverbal reasoning—in 74 ELLs in Grades 3 and 4. Path analysis revealed that comprehension of logical relationships was a direct predictor of concurrent reading skills in Grades 3 and 4, and an indirect predictor of reading comprehension in Grade 4, where vocabulary and prior comprehension performance acted as partial mediators. Results point to the confluence of general vocabulary with conjunctions in contributing to individual differences in ELLs’ reading comprehension. Conjunctions represent a specialized form of vocabulary knowledge that should not be subsumed developmentally or instructionally under general vocabulary knowledge.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".