The Effect of the Type of Pre-reading Tasks on the Reading Comprehension of Culture-Specific Texts
Bibliographic record
Abstract
Regarding the intimacy of language and culture in language studies and language education, teaching culture-specificities have drawn a lot of attention. It has always been a hotly-debated issue that “how” culture-specific points should be taught. This article is an attempt to propose a model for the facilitation of this “culture transference”. This study particularly addresses teaching culture-specific texts. To this end, 60 students were randomly selected and assigned into 3 homogeneous classrooms (A, B, C). The same culture-specific passages were presented to these groups, in a variety of pre-reading modalities: pictorial context condition, vocabulary pre-teaching and no pre-reading. The results of the study indicated that culture-specific texts taught in pictorial context condition are grasped better than those taught by vocabulary pre-teaching and no pre-reading. This outperformance of pictorial context learners compared to the other two groups can be justified using schema theory and dual coding theory (DCT). The results seem to be of relevance and help for pedagogical purposes in the setting of Iranian L2 classroom.
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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 | 0.002 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".