Interaction between levels of text representation and working memory during L2 reading comprehension: What about it?
Bibliographic record
Abstract
Abstract The relationship between L2 reading comprehension and working memory has been studied for years, and previous studies highlight the existence of a correlation between the two. However, to our knowledge, no previous study used Kintsch's Multilevel Comprehension Model to operationalize reading comprehension in the investigation of its relationship with working memory. More specifically, according to Kintsch's model, comprehension consists of three text representation levels—the surface level (the literal wording of the text), the textbase (which includes inferences made by the reader), and the situation model (the integration of explicit and implicit text information with readers’ background knowledge). Therefore, the study reported in this paper examined the contribution of working memory, the short‐term retention of information and its manipulation, to different text representation levels during L2 reading comprehension. To do so, fifty‐five (N = 55) adult L2 learners of French completed L2 reading comprehension task tapping into three levels of text representation and a numerical complex working memory task. The results showed, on the one hand, a significant contribution of working memory to L2 reading comprehension and, on the other hand, that this relationship was specifically observed with the situation model.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".