Vowel Blindness and Gender: The Case of ESL Learners at the University of Jeddah
Bibliographic record
Abstract
Because vocabulary knowledge is considered the building block of language learning, any difficulties concerned with vocabulary can harm the overall vocabulary acquisition process. Literature suggests that native Arabic speakers struggle to notice vowels while reading English texts. This can result from the differences between L1 and L2 linguistic systems or the negative transfer of L1 processing routines to the L2 in their attempt to process the L2 forms. This study investigates this problem and whether gender affects this phenomenon or not. It used a test on Twenty-eight participants to examine the effect of gender on vowel blindness, which type of vowels (short or long vowels) are more problematic, and the kinds of vowel spelling errors that are easily noticed when processing vowels. The results showed that gender does not affect vowel blindness in a significant way. In other words, the overall results showed that the role of gender cannot be considered to have a significant effect. Thus, male and female students both struggled to deal with short vowels equally. However, males also showed difficulties regarding the long vowels. The research also revealed that missing vowel spelling errors are salient and more likely to be noticed while processing vowels for both genders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".