Analysis of Gender Representation in English Language Learning Materials: The Case of Grade Ten Textbook in Ethiopia
Bibliographic record
Abstract
The main purpose of this study was to explore gender representation in grade ten English textbook. The study employed content analysis approach which was based four categories of analysis such as language use, visibility/ illustrations, occupational roles and firstness. The units of analysis were words, phrases, sentences, paragraphs, passages, stories and illustrations in the materials. The data was analyzed in frequency count and compared using Chi-square test to determine the level of significance of the differences obtained between the masculine and feminine groups observed in each category. The findings disclosed that females were underrepresented in language use (particularly in proper nouns and common nouns used), visibility/ illustrations (images and pictures) and occupational roles mentioned in the text. In addition, males dominated the first position (firstness) in dialogues, points of view opinion, common noun pairs, pronoun pairs and proper name pairs. On the contrary, males were underrepresented in the adjectives and pronouns used in the text book. In general, the textbook was characterized by unfair representation of gender in all aspects.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".