Elite career expectations of adolescents: Popularity, gender differences, and social divides
Bibliographic record
Abstract
INTRODUCTION: The supply of elite professionals is crucial for economic development, yet little is understood about the appeal and influencing factors of these careers among young people across different economies. It remains unclear whether adolescents in academically high-performing economies growingly expect emerging technological jobs in response to evolving workforce demands. METHODS: This research used the Programme for International Student Assessment 2000-2018 data in 24 high-performing educational systems to examine the two-decade trends in adolescents' expectations for Science, Technology, Engineering, and Math (STEM), medicine, law, business, and teaching careers. The popularity trend of these careers and the major impacts of gender, socioeconomic status, and academic ability were examined with multilevel logistic regression models. RESULTS: The findings indicated that developed economies such as Singapore, Canada, the United States of America, and the United Kingdom have successfully attracted a greater proportion of students to elite careers. In contrast, many high-performing Asian economies, such as Korea, Japan, and Taipei, have not. STEM and medical fields primarily drew students with high math abilities, whereas legal professions attracted those with superior reading skills. Although girls generally expected teaching and legal careers and boys expected STEM fields, social and gender differences have narrowed over the past decades. CONCLUSIONS: Many Western developed economies effectively attract a larger share of adolescents to STEM careers than their Asian counterparts. Although gender and social disparities persist, their impact has diminished. Effective human resource planning should be based on each country's unique trends and influencing factors to promote greater equality and inclusion in the workforce.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".