Choosing an English Teacher: The Influence of Gender on the Students’ Choice of Language Teachers
Bibliographic record
Abstract
<p>Gender and teaching are gaining increasing attention in the field of higher education. The significance of teacher gender seems even more crucial in an environment based on gender segregation. In the scope of language teaching and gender, this study investigates the influence of gender on the students’ selection of teachers in general, and language teachers more specifically. The participants, 146 English major students in an all-female college of education, were given a questionnaire of 32 statements--to be answered on a 5-point likert scale--and four open-ended questions; all of which aim at examining the difference between male and female English language teachers in terms of attitude, grades, teaching and even appearance. The statistics were analyzed in terms of frequency, mean and variance in correlation with the independent variables of age, social status, GPA and years in college. It was found that most students prefer male teachers as they believe that the positive personal traits of the male teachers far exceed those of the female teachers. Nonetheless, the statistics have revealed that both genders (and sometimes female more than male teachers) are good language teachers. Hence, reflecting the main finding: gender is not a criterion for good language teaching, but it is our students’ criterion for choosing a language teacher.</p>
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".