Does Gender Play a Role in the Assessment of Oral Proficiency?
Bibliographic record
Abstract
Gender has been a controversial issue which affects the language learning process. McNamara (1996) has proposed that there are some variables affecting second language performance one of which is sex. In much the same way, it has been reported that gender plays a role in the area of language testing (Brown, 2003; Lumley & O’Sullivan, 2005; Motallebzadeh, 1993; O’Sullivan, 2002).The present study is, thus, an attempt to explore the possible relationship between gender and oral performance of Iranian intermediate and upper intermediate EFL language learners. For this purpose, 429 adult students in six different institutions in Mashhad and Kerman participated in the study. After the Oxford placement test and an IELTS-format oral placement test, 160 of them were selected for a final oral interview. Finally, through a T-test, it was found out that females did better in oral performance than males, however, the difference was not that significant.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".