Public vs private sector employment
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the factors that may be related to a career choice in the public vs the private sector in a developing African country. Design/methodology/approach Using a sample of graduate management students, the authors tested reward preferences and altruism, elements of public service motivation, on their generalizability to a developing country in Africa. The authors also examine the role of career attitudes, individual personality factors, and cultural values on a career choice in public service. Findings The authors find that not all the factors associated with the choice of sector (public or private) found in previous studies apply in the Botswana context. Research limitations/implications Perry and Wise (1990) developed the concept of public service motivation to explain why individuals may be motivated to serve the public. However, two of the factors associated with public service, intrinsic motivation, and altruism, were not predictive of a career choice in the public sector in Botswana, and thus may limit its generalizability outside of western developed countries. Practical implications In Botswana and other developing economies, government jobs are considered to provide lucrative and stable employment, and attract educated citizens regardless of motivations. However, as the private-for-profit sector is emerging, these countries could soon be facing serious competition for top university students, and will need to develop a strategy for attracting the best talents to choose employment in the public sector over career options in the private sector. Originality/value The present study seeks to further the understanding on how individuals make a career choice between public vs private sector management in a developing country.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".