Reading Comprehension of Different Genres: A Fuzzy Approach
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
This study presents a model based on fuzzy logic with two inputs: idea units and main ideas. The inputs were gained by scoring reading comprehension of 19 MA students at Shiraz University who were each given three different genres. The data were fed into the fuzzy system and fuzzy scores were obtained as the output. First, the same participants’ understandings across different genres and then, different participants’ understandings across the same genre were compared. And finally, the number of idea units referred to by participants was calculated to see which idea units were problematic. The results of comparing fuzzy scores and scores resulting from idea units indicated that fuzzy scores were fairer, and more accurate. The results of paired t-test showed that the narrative genre was easier than the descriptive and argumentative genres. Also, the results showed that the same participants had different degrees of understanding across genres and different participants had different degrees of understanding in the same genre.
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 | 0.000 | 0.008 |
| 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 | 0.001 | 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