Computers and Career Choices: Gender Differences in Grades 7 and 10 Students
Bibliographic record
Abstract
Knowledge of mathematics and the sciences is an essential prerequisite in the pursuit of high-status and well-paid jobs in a technologically advanced workforce. However, there is increasing evidence that this kind of expertise will not keep pace with the demands anticipated in the 21st century. Research that investigates the relation between school culture, socialization, ability, gender and values and the relative degree of influence on adolescent student choice in courses, programs, activities in general, and in science and technology specifically, would contribute significantly to our understanding of the problem. Eccles model on achievement-related choices in education and career decisionmaking was utilized in the present research. The focus of this article is a report on gender by grade comparisons on several questions pertaining to computer interest and usage, and student choices concerning desirable career characteristics, future plans and likely career choices. Results indicate several significant grade and gender differences. Of particular note are the future career interests of the girls compared to the boys whereby, in general, these career interests are falling along traditional paths.1
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".