Analysis of Gender Representation in English Language Learning Materials: The Case of Grade Ten Textbook in Ethiopia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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