Am I a peasant or a worker? An identity strain perspective on turnover among developing-world migrants
Bibliographic record
Abstract
Developing-world rural migrants provide crucial labor for global supply chains and economic growth in their native countries. Yet their high turnover engenders considerable organizational costs and disruptions threatening those contributions. Organizational scholars thus strive to understand why these workers quit, often applying turnover models and findings predominantly derived from the United States, Canada, England or Australia (UCEA). Predominant applications of dominant turnover theories however provide limited insight into why developing-world migrants quit given that they significantly differ from UCEA workforces in culture, precarious employment and rural-to-urban migration. Based on multi-phase, multi-source and multi-level survey data of 173 Chinese migrants working in a construction group, this study adopts an identity strain perspective to clarify why they quit. This investigation established that migrants retaining their rural identity experience more identity strain when working and living in distant urban centers. Moreover, identity strain prompts them to quit when their work groups lack supervisory supportive climates. Furthermore, migrants’ adjustment to urban workplaces and communities mediates the interactive effect of identity strain and supervisory supportive climate on turnover. Overall, this study highlighted how identity strain arising from role transitions and urban adjustment can explain why rural migrants in developing societies quit jobs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".