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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.010
Science and technology studies0.0040.005
Scholarly communication0.0060.006
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 source (direct Gemma or distilled Codex), not a consensus.

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

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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