Gender and Professional Career Plans of High School Students in Comparative Perspective
Bibliographic record
Abstract
The time when young women left the labour force upon marriage, and thus had much more modest educational and occupational expectations than young men, belongs to history. Recent studies in the USA and Canada show that at 15, girls now plan to attain higher levels of education and are more determined to enter professional careers than boys. We seek to establish whether this is the case in many different cultures and socio-economic conditions. To this end, we analyse the data from the 2006 round of the OECD’s Program for International Student Assessment (PISA), conducted in over 50 countries. First we establish whether girls are more ambitious than boys across countries when we control for the variation in academic ability, home and school environments. Second we examine the possibility that the attraction to professional occupations can be explained by gender-typed choices, i.e. girls ’ preference for nursing and teaching versus boys ’ determination to enter trades. Third, we examine how school characteristics, i.e. the proportion of female students, school resources, socio-economic characteristics of parents as well as the macro-social contexts, i.e. labour market opportunities open to women, may help girls set higher achievement goals. Finally, we consider how likely are girls, compared to boys, to answer questions about career plans, because any gender bias in the patterns of missing data might affect the overall conclusions. We conclude by discussing the implications of these gender differences, with a special focus on a dilemma they may pose for policy makers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".