Bibliographic record
Abstract
Tis volume is unique in its examination of the impact of Xi Jinping's leadership across a broad scope of issue areas.When Rongbin Han and I commenced planning for the project at the American Political Science Association's annual conference in 2018, no one had done anything quite like it, though scholars had written about Xi's family background, views on governance, and experiences prior to becoming the most powerful Chinese leader since Deng Xiaoping.To supplement our own skills, we needed to recruit other scholars with diverse disciplinary backgrounds.Researchers working on the project had to have sufcient expertise to consider Xi's efect-not simply on political life-but also on related topics: the anti-corruption campaign, poverty alleviation, economic inequality, religion, public service provision, state surveillance, Han-minority tensions, and China-Taiwan relations.Consequently, we encouraged scholars worldwide to submit paper proposals and hosted the "Xi Jinping Efect" international conference at the Banf Centre in Alberta, Canada, in 2019.Six of the conference papers were published in the Journal of Contemporary China, volume 30, numbers 131 and 132 (2021).One of those, "Xi Jinping's Counter-Reformation: Te Reassertion of Ideological Governance in Historical Perspective" by Timothy Cheek, is included here with permission of Taylor & Francis.Other papers, which complicate and even problematize the Xi efect, were reserved and developed for publication in this book.We solicited additional chapters to fll important gaps and wrote an introductory overview, and Kevin J. O'Brien drafed a concluding chapter, based on his insightful commentary at the conference.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.239 | 0.134 |
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".