Bibliographic record
Abstract
Sexism and the ESL Teacher: Restarting the DiscussionIn our patriarchal society there is still evidence of inherent sexism in every aspect of life.This perspective essay concentrates on the sexism that is inherent in the English language.Although this ongoing debate may be considered by some as outdated, I disagree.In writing this essay I attempt to bring to the forefront an important issue and, I hope, to restart discussions.I do not assume the authority of summing up the whole debate, but rather would like to reintroduce ESL teachers to the subject and to sensitize them to covert assumptions and overt practices that seem to sustain sexism.As teachers and professionals we have the responsibility to continue learning and growing, to search for, develop, and offer our students our best teaching practices.We need to assess not only our students, but ourselves as well.Through observations of, and reflections on, our own teaching procedures, we may want to redefine our goals and practices to include nonsexist material, language, and literature.Numerous publications that deal with sexism and nonsexist teaching are available from departments of education and teachers' associations.To draw a connection between the work we do as ESL teachers and the problems women face in our society today can be a daunting task.Questioning some of our views and values requires strength.However, it is an important aspect of our work as ESL teachers if we wish to give all our students the necessary tools to be whole, competent, and dynamic members
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".