Bibliographic record
Abstract
Purpose What are the mechanisms through which Chinese municipal leaders overcome implementation breakdown? This study, through process tracing, archival work and semi-structured interviews, examines the implementation of three sub-municipal-level railway projects involving the same principals and agents over the same period of time. Design/methodology/approach The analysis was guided by the hypothesis that political coordination and the exercise of political and Party leadership played an indispensable role in the two cases of successful policy implementation, and its absence accounts for the case of implementation breakdown. Findings The principal finding is that an informal “strategic group” was created to “herd” cadres to overcome the problem of implementation. Herding here refers to the idea that Party leadership, through the use of moral persuasion, encourages cadres moving towards a desired common goal and direction. Research limitations/implications This study is limited in the number of secondary resources (government documents and government and media releases) available to the field interviewees, which the author heavily relied on to complete the study. Originality/value Building on the conceptual work of “strategic groups” by Thomas Heberer, Anna Ahlers, and Gunter Schubert, this study makes an empirical contribution by tracing the process through which an informal strategic group exercises its power to overcome implementation breakdown.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".