Bibliographic record
Abstract
Since 1949, China has tried a large number of successful and not-so-successful corporatist experiments. What these phases have in common is a ‘state corporatist’ (top-down) approach, albeit with shifting degrees of state involvement; and in the present era, a snail’s pace effort towards building a ‘societal corporatism’ (bottom-up). Over the same time period, we have also witnessed a shift from a centrally controlled corporatist state to one in which the local state has greater space to implement corporatist techniques – allowing the formation of business and professional associations at the local level, and providing them a space for local state-directed bargaining. As China embarks on its next decade, a consideration of Chinese corporatism is useful in two respects. First, foreign corporations and governments who engage in China, particularly if they come from pluralist-competitive societies, tend to misunderstand the nexus of businesses, organizations, and the state. They wrongful presume that state direction and corruption are synonymous. A more nuanced understanding of Chinese corporatism, however, leads to an important second point – that some lessons learned by Chinese experiments in corporatism, during the very period of its ascent to becoming an economic superpower, may be beneficial for foreign policymakers considering paths to a more stable public 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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".