Analysis of Influential Factors for High School Students’ Career Choice: Evidence from Canada
Bibliographic record
Abstract
Making a career choice is an important phase in every student’s life. Before making decision, students must consider several factors. This research is supposed to investigate the factors that may affect career choice. By collecting data from Statistics Canada, anonymous experimental questionnaires, and comparing and analyzing statistical and mathematical models such as standard deviation and Inter quartile range, this paper concludes five important factors that influence students' career choices: gender, interest, personality, environment, and opportunity. By studying each factor on a case-by-case basis, it can be concluded that first, in terms of gender's influence on career choices, boys tend to choose technical positions and girls tend to choose service positions. Second, Satisfaction, Security & Motivation is the most important factor in the issue of interest influencing students' career choices; Third, among personality, environment and opportunity, these three factors, personality is the biggest factor that affect the students' career choice. These conclusions inspire education that when guiding students to employment, they should pay more attention to students to examine their own personality. For students, when observing the interest in career choices, they can start with satisfaction, security, and motivation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".