When Western HRM constructs meet Chinese contexts: validating the pluralistic structures of human resource management systems in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study takes a contextualized approach to examine the structures, measures and predictive value of four human resource management (HRM) systems in China. Synthesizing the established concepts in the Western literature with the indigenous practices in the Chinese workplace, we contextually adapt the conceptual components of commitment-based, collaboration-based, controlled-based and contract-based HRM systems. Using data from 224 organizations, we found a pluralistic structure of the HRM systems, consisting of two discriminant dimensions within each HRM system, and a high-order model encompassing all HRM systems. In addition, we found different predictive value of the HRM systems firm innovation, cost reduction and bottom-line performance. Ideas for future research and practical implications are also discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it