Factors Affecting Gender Equity in the Choice of Science and Technology Careers among Secondary School Students in Edo State, Nigeria
Bibliographic record
Abstract
The study investigated the factors affecting gender equity in science and technology among senior secondary school students. The study was carried out at the University of Benin Demonstration Secondary School in Benin City, Edo State, Nigeria. One hundred and fifty students of average age 15 years in their penultimate year were administered the questionnaires for the study. The data for the study was collected from a survey instrument titled, “Career Determinants. Analysis of the data revealed that sex, parental, peer influences, social and cultural stereotyping were the major factors affecting gender inequity in the choice of careers in science and technology among secondary school students. Less than 40% of the girls indicated interest in science and technology subjects even though they had the ability. More than sixty-five percent of the boys indicated interest in science and technology subjects even though they were not academically prepared for them. It was therefore suggested that gender equity in science and technology could be fostered by designing a training program in science to build confidence and assertive skills in students at the junior secondary school level. Recommendations were also made that seminars should be conducted for parents and teachers in primary schools to desensitize stereotyping acquired through socialization processes and cultural practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".