Critical Qualitative Inquiry in China Studies: Introduction to the Special Issue
Bibliographic record
Abstract
This Special Issue aims to advance critical qualitative inquiry in China studies and contribute to a vibrant, inclusive global community. It builds upon debates and efforts in the behavioral and social sciences among area specialists in two eras: researchers in Taiwan, Hong Kong, and the diaspora in the 1980s who sought to sinologize behavioral and social sciences, and sociologists in China in the 2000s who are seeking to indigenize these fields. The Issue takes a two-pronged approach toward advancing critical reflection in knowledge production: (a) it aspires to diminish the current influence of Western and positivistic paradigms on behavioral and social sciences research; (b) it seeks to challenge discursive hegemonic influences to create and sustain space for critical qualitative inquiry. The Issue traverses disciplinary boundaries between history and behavioral and social sciences within China Studies. It opens dialogue with the non-area specialists who are the primary audience of the Qualitative Inquiry.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".