Fitting Throughout the Employment Cycle: PE Fit Before, During & After Organizational Entry & Exit
Bibliographic record
Abstract
This symposium showcases a collection of research on the breadth of the employee fit experience. Research in this session considers the role of fit as workers enter into, build successful work experiences within, and move between organizations. Specifically, we explore how demographic and psychological individual differences impact the ways employees assess and increase fit during job search; identify the various strategies employees adopt to manage misfit at work; and investigate the unique ways that employees experience fit with various aspects of their work across subjective time. We provide multiple perspectives on the fit and misfit experience, demonstrating how these experiences impact important individual and organizational outcomes, such as attraction, job acceptance, retention, and withdrawal. This session will help researchers and practitioners alike better understand how to effectively manage employee fit as a strategy to tackle the novel challenges associated with the modern labor market. From a Gendered Lens Perceptions of Fit during Recruitment Author: David W. Sullivan; U. of Houston Author: Horatio Traylor; U. of Houston Author: Joel Andrus; U. of Missouri How Calling Orientation Shapes Reemployment Crafting Behaviors During Furlough A Diary Study Author: Shuai Ren; McMaster U. Author: Yair Berson; McMaster U. Author: Aaron CH Schat; McMaster U. A Typology and Scale Development for Employee Misfit Navigation Strategies Author: Christina Li; U. of Oklahoma Author: Qi Zhang; Oregon State U. All the Time, All at Once A Latent Profile Analysis of Subjective Temporal Fit Trajectories Author: Kristina Tirol-Carmody; U. of Kansas Author: Christina Li; U. of Oklahoma Author: Daniel Goering; Missouri State U.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".