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Record W4415537585 · doi:10.1177/2057150x251385829

Chinese perspectives on organizational research: A summary of the 20th Workshop on Empirical Research in Organizational Sociology

2025· article· en· W4415537585 on OpenAlexaff
Jihao Yu

Bibliographic record

VenueChinese Journal of Sociology · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsOrganizational studiesEmpirical researchOrganization developmentOrganizational learningOrganizational analysisOrganizational behavior and human resourcesOrganizational commitmentGovernment (linguistics)China

Abstract

fetched live from OpenAlex

Since its inception in 2004, the Workshop on Empirical Research in Organizational Sociology has propelled the rapid development of organizational sociology in China through sustained academic exchanges. This progression has led to a wealth of outstanding research achievements and the emergence of an influential academic community. The 20th Workshop covered eight key topic areas: government behavior, local governance, social governance, technological governance and organizational strategies, development of organizational theory and practice, trust and emotions, social organizations, and industry and education. These presented papers, which focused on significant organizational phenomena amid contemporary social transformations in China, promoted continuous theoretical and methodological innovation in organizational research. The 20th Workshop remained committed to its founding mission of providing a platform for in-depth academic discussions, especially for early-career researchers, while fostering an open and dynamic environment for the advancement of organizational theory.

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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.070
GPT teacher head0.396
Teacher spread0.326 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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