Career regret and career sacrifice: the less examined yet ever-present career experiences
Bibliographic record
Abstract
This symposium explores career regret and career sacrifice – phenomena experienced by many individuals throughout the course of their working lives. Career regret is the feeling of wishing to undo a past decision made in one’s career, whereas career sacrifice involves a decision to give something up in one’s career. These related career experiences have both been relatively under-explored in the field of careers and organisational behaviour (Byington, Felps and Baruch, 2019). However, their prevalence and their potential to impact on the careers and well-being of career actors, especially in the current turbulent climate, calls for a better understanding of these phenomena. Scholars in this symposium have been united in their motivation to further understand these phenomena and their individual and organisational consequences The Remains of the Day: Dealing with Regret in Later Career Presenter: John Blenkinsopp; Newcastle Business School, Northumbria U. Presenter: Shuo Wang; Newcastle Business School, Northumbria U. Presenter: Olaolu Eniola; Newcastle Business School, Northumbria U. Regret’s consequences: The performance of Regret Workers Presenter: Alexandra Budjanovcanin; King's College London Presenter: Chris Woodrow; Henley Business School, U. of Reading Higher role performance… but higher partner self-sacrifice too. Does worker self-sacrifice pay off? Presenter: Michael Clinton; King's College London Unpacking career sacrifice: From prosocial benefits to regret Presenter: Jelena Zikic; York U. Presenter: Soodabeh Mansoori; York U., Toronto
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".