Tackling Inhibitions to Careers in Science and Technology through Differentiated Mentoring Approach
Bibliographic record
Abstract
Encouraging women to go into Science and Technology (S&T) careers should start with the young girls. In developing countries, such as Nigeria, girls experience challenges in studying science and technological subjects and pursuing careers in these professions. The study identifies factors that inhibit Nigerian girls from undertaking careers in S&T. A sample of 228 Nigerian Senior Secondary School girls was used for the study. A “Female Students Science and Technology Inhibitions Questionnaire” (FSSTIQ) was used to elicit responses from the girls on conceptual, psychological and physical inhibitions to their studying S&T subjects. Percentages, mean and standard deviation were used to describe the data obtained. The results reveal that the major conceptual, psychological and physical problems the girls encountered were mathematical concepts, perception of S&T subjects as being difficult, and inadequate time to study. A “Differentiated Mentoring” approach is recommended for engendering effective mentoring of school girls interested in pursuing science and technology careers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".