Exploring New Directions for Theorizing About Career Shocks
Bibliographic record
Abstract
It is abundantly clear that careers have become highly unpredictable and volatile, implying that scholars must include significant disruptions when studying contemporary career development and success. As a result, research on career shocks has flourished in recent years. However, the career shocks literature lacks comprehensive theorizing of how career shocks impact people’s work, careers, and lives. We argue that now is the time to develop and share such theories. Therefore, this symposium brings together five different studies that contribute uniquely to new theorizing on career shocks. Career Shocks: A Psychological Process Model Author: Scott Seibert; University at Buffalo, School of Management Author: Mel Fugate; Mississippi State University Author: Jos Akkermans; Vrije Universiteit Amsterdam Author: Maria Kraimer; University at Buffalo, School of Management Author: Stefan Thomas Mol; University of Amsterdam Career Shocks and Chance Events Role and Impact on Careers: A Career Ecosystem Perspective Author: Yehuda Baruch; University of Southampton Intersecting Shocks: Advancing Career Shocks Theory through the Lens of Race and Gender Author: Holly Slay Ferraro; Villanova University Author: Jennifer Ann Marrone; Seattle University Career Shocks on Reddit: How Mass Layoffs Impact Employees Author: Claudia Christina Kitz; University of Groningen Author: Anita Keller; University of Groningen Author: NANXI Yan; University of Amsterdam Agency Constraints and Bounded Rationality in Career-Shock Contexts: Advancing a Theory of Aberrant Career Navigation Author: Matthew B. Perrigino; Baruch College of the City University of New York Author: Ariane Ollier-Malaterre; Université du Québec à Montréal (UQAM) Author: Marcello Russo; University of Bologna
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".