Navigating Career Transitions: Early Development, Alternative Paths, and Late-Career Perspective
Bibliographic record
Abstract
While successful career transitions are vital for overall career quality, those transitions are complex and can be challenging. Occurring at different career and life stages, workers need to repeatedly maneuver transitions throughout their career journeys, ranging from school to work until retirement. Amidst the evolving modern career landscape, also our understanding of careers is shifting, and workers progressively seek to, e.g., experience meaningfulness and realize work flexibility. While research provides valuable insights into how people successfully manage diverse career transitions, we lack knowledge on what influences workers’ career choices and their experiences when transitioning across key career stages. This symposium integrates research on key career stages, ranging from school-to-work transitions, to people moving in between their work and leaving traditional paths of employment to seek fulfillment in alternative careers, to the late career stage and retirement. By this, we explore how workers experience and thus choose to develop their careers, how they make career choices to better align their careers with their identities and with what is important to them, and how they navigate new, alternative, and difficult career transitions. We advance knowledge on career transitions and offer a comprehensive view of how workers maneuver and experience their career journeys. Setting Sails for your Harbor: Exiting NEET Status through Self-Efficacy and Career Decidedness? Author: Gloria Willhardt; Justus-Liebig-U. Giessen Author: Ute-Christine Klehe; Justus-Liebig U. Giessen Author: Miriam Schäfer; Justus-Liebig-U. Giessen ‘Work Moves’ in the New World of Work: Examining the Role of Self-Narratives and Significant Others Author: Jordan Nye; U. of Michigan, Ross School of Business Now You See It, Now You Don’t: Is Meaningfulness Sought or Discovered in Career Change? Author: Elise B. Jones; US Coast Guard Academy Author: Christina Hymer; U. of Tennessee, Knoxville Alternative Career Pathways of Skilled Migrants: Looking for New Meanings amid Starting Again Author: Soodabeh Mansoori; York U., Toronto Author: Jelena Zikic; York U. Utilizing Uncertainty Regulation as a Late Career Strategy Author: America Harris; U. of Hohenheim Author: Meghan Davenport; U. of North Carolina at Charlotte Author: Ulrike Fasbender; U. of Hohenheim
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".