The Data Frontier: Expanding Empirical Horizons in Chinese Management Research
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
Abstract This editorial examines the empirical foundations of Chinese management research through an analysis of data sources and research designs in all empirical papers published in Management and Organization Review (MOR) over the past five years. Our review shows that 53.2% of studies rely on archival or secondary data, with 37% of quantitative studies focusing on publicly listed firms. While established datasets provide consistency and comparability, their prevalence may limit opportunities to explore China’s diverse organizational ecosystem. We identify three promising avenues for advancing the field: (1) expanding empirical attention to include a wider variety of organizational forms, (2) leveraging emerging computational methods, digital trace data, and AI-enabled technologies, and (3) recognizing the development of novel datasets as valuable scholarly contributions in their own right. We also examine how recent regulatory developments are creating new considerations for research design while reinforcing the value of collaborative approaches between international and Chinese scholars. We contend that by embracing methodological pluralism and adapting to evolving data landscapes, management scholars can generate additional novel insights that illuminate the complexity and distinctiveness of Chinese organizational life.
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.
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 | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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