Career Satisfaction: A Look behind the Races
Bibliographic record
Abstract
Previous studies have largely focused on the career success of white employees (Heslin, 2005). Using recent survey data, this paper examines the career satisfaction levels of white/Caucasian and visible minority managerial, professional and executive employees in the information and communications technology [ICT] and financial services sectors in corporate Canada. Given that the demographic makeup of organizations in Canada is drastically changing with the aging population and the increasing participation of visible minorities in the labour force, it is crucial for managers and organizations to understand their employees’ level of career satisfaction. Studies have found that employees who are more satisfied with their careers are more engaged and thus are more likely to actively contribute to the organization’s success (Peluchette, 1993; Harter, Schmidt and Hayes, 2002). Findings from this paper showed that the average career satisfaction scores were lower for visible minority employees than for white/Caucasian employees. In addition, variations were found between white/Caucasian employees and Chinese, South Asian and Black visible minority employees. While Black employees were 13.0% less satisfied than white/Caucasian employees, Chinese employees were only 8.3% less satisfied than their white/Caucasian counterparts, and the difference between South Asian and white/Caucasian employees was found to be insignificant. Decomposition analyses show that over 58% to 82% of the difference in career of satisfaction scores, depending on the ethnic group, can be accounted for by factors included in this paper. Of the unexplained portion, most of the differences in career satisfaction between white/Caucasian and minority groups are attributable to higher returns to white/Caucasian employees’ human capital and demographic characteristics.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".