Careers in the Greek public sector: calibrating the kaleidoscope
Bibliographic record
Abstract
Purpose The authors draw upon the kaleidoscope career model (KCM) to explore the career perceptions of public service employees in Greece. Design/methodology/approach Qualitative semistructured interviews were conducted with 33 civil servants. Findings The authors’ demonstrate how context frames career perceptions and propose an additional KCM parameter (security). Research limitations/implications This context-based study proposes an extension of the KCM theory beyond the original three parameters that were dominant at its inception. Practical implications The authors provide recommendations for human resource practices, such as empowerment through training, fair promotions and providing meaning. Despite the common perception, the need for challenge exists even within the public sector, such that satisfying it can help organizations to gain strategic advantage. Originality/value This study expands a prominent career theory by exploring it in a unique context. By doing that, the authors are able to better understand how the parameters of the model are readjusted in different settings and to uncover a previously unidentified theme.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".