Worker Capture in the Precarious Work Era: Reducing Turnover without Employee Commitment and Job Security
Bibliographic record
Abstract
This paper argues that employee retention remains a key priority for many employers, even as they become less likely to offer stable employment. In the face of increased job insecurity, researchers have shown that workers have adopted personalized career progression strategies that emphasize inter-firm mobility. Such strategies heighten employer concerns about employee turnover. The paper argues that the industrial relations literature would gain from closer study of organizational practices that restrict employee mobility, i.e., “worker capture strategies.” Such practices stand out from other mobility-reducing ones because they are unilateral and aim to reduce turnover without necessarily fostering a sense of commitment or loyalty among employees. Summary The literature on precarious work has focused on the higher frequency of layoffs and downsizing and the shift away from the standard employment relationship over the past few decades. This paper argues that employee retention remains nonetheless a key priority for many employers, even as they become less likely to offer stable employment. In the face of increased job insecurity, researchers have shown that workers have adopted individualized career progression strategies in which inter-firm mobility plays a great role. Such strategies heighten employer concerns about employee turnover. This paper reviews the state of the literature on organizational practices that address employer concerns about employee retention. It then argues that this literature would gain from closer study of organizational practices that restrict employee mobility, which it labels “worker capture strategies.” They include non-compete and non-solicitation clauses in employment contracts, no-poaching agreements between firms, and TRAP clauses. Such practices stand out from others because of their one-sided nature and their use as a turnover reduction strategy that does not necessarily drive a sense of commitment or loyalty among employees.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".