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Record W3021788970 · doi:10.1108/aeds-10-2019-0164

Strategic groups and local railway development in China

2020· article· en· W3021788970 on OpenAlexaff
Karl Yan

Bibliographic record

VenueAsian Education and Development Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsOriginalityGovernment (linguistics)Process tracingWork (physics)Public relationsPoliticsValue (mathematics)Political scienceChinaProcess (computing)EngineeringCreativityComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.308
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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