Pictures Speak Louder than Words in ESP, Too!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While integrating visual features can be among the most important characteristics of English language textbooks, reviewing the current locally-produced English for Specific Purposes (ESP) ones reveals that they lack such a feature. Enjoying a rich theoretical background including Paivio’s dual coding theory as well as Sert’s educational semiotics, this research was done to investigate the probable effectiveness of using pictorial context in ESP reading comprehension ability of Iranian university students whose syllabus mostly focuses on this skill. To do so, this study was conducted on two groups of Iranian students majoring physics. Before the treatment, pretest was performed in both groups. The students in the experimental group were taught through passages furnished with different kinds of pictures while the ones in the control group were taught through the same passages without the pictorial context. At the end of the treatment which took twenty two sessions of two hours during twelve weeks, the posttest was administered to both groups. At the end, drawing on t-test at the significance level of 0.05, the students’ performance was compared. The results revealed that there was a significant difference between the mean score of the two groups. Thus, it was concluded that using pictorial context improves the ESP reading comprehension of students.
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it