Intelligent careers and human resource management practices: qualitative insights from the public sector in a clientelistic culture
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this study is to empirically test the intelligent career framework in a public sector setting in a country with a clientelistic culture to inform human resource management strategies. Design/methodology/approach Based on a qualitative methodology and an interpretivist paradigm, 33 in-depth interviews were conducted with Greek civil servants before the COVID-19 pandemic. The interview recordings were subsequently transcribed and coded via a blend of inductive and deductive approaches. Findings Outcomes of the study indicate that in a public sector setting in a country with a clientelistic culture, the three dimensions of knowing-whom, knowing-how and knowing-why are less balanced than those reported by findings from private sector settings in countries with an individualistic culture. Instead, knowing-whom is a critical dimension and a necessary condition for career development that affects knowing-how and knowing-why. Originality/value The theoretical contribution comes from providing evidence of the dark side of careers and how imbalances between the three dimensions of the intelligent career framework reduce work satisfaction, hinder career success and affect organisational performance. The practical contribution offers recommendations for human resource management practices in the public sector, including training, mentoring, transparency in performance evaluations and fostering trust.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it