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Record W4206714894 · doi:10.1093/rof/rfab023

Going Bankrupt in China

2021· article· en· W4206714894 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Finance Review · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsKellogg's (Canada)
FundersNational Natural Science Foundation of China
KeywordsStylized factBankruptcyInsolvencyChinaProductivityCapital (architecture)BusinessIndependence (probability theory)Total factor productivityIdentification (biology)Judicial independenceEconomicsFinancePolitical scienceLawMacroeconomicsGeography

Abstract

fetched live from OpenAlex

Abstract Using a new case-level dataset, we document a set of stylized facts on bankruptcy in China and study how the staggered introduction of specialized courts across Chinese cities affected insolvency resolution and the local economy. For identification, we compare bankruptcy cases handled by specialized versus traditional civil courts within the same city and filed in the same year. We find that specialized courts decrease case duration by 36% relative to traditional civil courts. We provide evidence consistent with court specialization increasing efficiency via selection of better trained judges and higher judicial independence from local politicians. We document that cities introducing specialized courts experience a relative reallocation of employment out of zombie firms-intensive sectors, as well as faster firm entry and a larger increase in average capital productivity.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.712
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.017
GPT teacher head0.214
Teacher spread0.196 · 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