Linguistic Turn and Gendering Language in the Cambridge Advanced Learner’s Dictionary
Why this work is in the frame
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Bibliographic record
Abstract
<p>Language constructs how humans perceive things. Since language is a human construction, it tends to be biased as it is mainly men’s construction. Using gender perspectives, this paper attempts to discuss the imbalance in gender representations found in the examples given in an English learner’s dictionary, that is, the <em>Cambridge Advanced Learner’s Dictionary, 3<sup>rd </sup>Edition</em>. A learner’s dictionary is chosen because it is where one can find and learn the meaning of words. The results show that linguistically speaking, English is still a highly patriarchal and gendering language where men are portrayed better than women. Women tend to be subjugated under men’s domination. Sexism and patriarchy still overshadow the meanings of words characterizing men and women. This means that men are still considered to be dominating women, despite the fact that the feminist movement has been going more than thirty years. Consequently, English language teachers should balance the gender bias by providing addtional materials that are gender neutral.</p>
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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