Predictors of Successful Reading Comprehension in Bilingual Adults: The Role of Reading Strategies and Language Proficiency
Why this work is in the frame
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Bibliographic record
Abstract
The current study investigated the type of strategies that English–French bilingual adults utilize when reading in their dominant and non-dominant languages and which of these strategies are associated with reading comprehension success. Thirty-nine participants read short texts while reporting aloud what they were thinking as they read. Following each passage, readers answered three comprehension questions. Questions either required information found directly in the text (literal question) or required a necessary inference or an elaborative inference. Readers reported more necessary and elaborative inferences and referred to more background knowledge in their dominant language than in their non-dominant language. Engaging in both text analysis strategies and meaning extraction strategies predicted reading comprehension success in both languages, with differences observed depending on the type of question posed. Results are discussed with respect to how strategy use supports the development of text representations.
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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) | 0.000 | 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 it