"Job Insecurity, Employability, and Turnover in the Face of Different Labor Market Conditions"
Bibliographic record
Abstract
Careers unfold within a bigger macroeconomic context rather than operating in a vacuum. In the last decades, labor markets and careers have changed enormously. The boundaryless career was introduced as a new employment principle, however, with a focus on contrasting organization-led versus self-directed careers. The importance of contextual factors on a macro level (e.g., economic conditions in a geographical area, labor market segmentation) has been largely underestimated. In line with this critique, the aim of this symposium is to systematically explore the interplay between individual and structural factors in shaping individuals’ careers. We shed light on job insecurity, employability and turnover as central and multiple facets of careers through a context- dependent lens. Besides using samples from various countries to account for different labor market conditions, we provide studies looking at time-dependent contextual influences to better understand the challenges for individuals and organizations posed by the economic crisis. With this symposium, we (re-)introduce boundaries to the career debate in times where economy, organizations and careers seem continuously boundaryless. Career Prospects Before and After the Onset of the Great Recession - A Multi-Level Perspective Presenter: Petra M. Eggenhofer-Rehart; WU Vienna U. of Economics and Business Presenter: Michael Schiffinger; WU Vienna U. of Economics and Business Predicting Career Transitions: Effects of Labor Market Situation and Individual Determinants Presenter: Angelika Kornblum; ETH Zurich Presenter: Dana Unger; ETH Zurich Presenter: Gudela Grote; ETH Zurich Employability of Temporary and Permanent Workers: The Importance of Symbolic Capital Presenter: Jasper Delva; KU Leuven Presenter: Anneleen Forrier; KU Leuven Presenter: Nele De Cuyper; KU Leuven How the Labor Market Situation Affects Employees' Trust in Organizations and Themselves Presenter: Wiebke Doden; ETH Zurich Presenter: Manuela Morf; U. of Zurich Presenter: Gudela Grote; ETH Zurich
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".