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Record W3123610114 · doi:10.1017/asjcl.2020.15

Replicating Silicon Valley? Law and Human Capital in the Making of China's Tech Startups

2020· article· en· W3123610114 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

VenueAsian Journal of Comparative Law · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChinaSilicon valleyReplication (statistics)High techBusinessMarket economyNorm (philosophy)PoliticsPolitical scienceEconomic systemPolitical economyLawEconomicsEntrepreneurship

Abstract

fetched live from OpenAlex

Abstract The rise of China's tech companies in the global economy raises an urgent need to understand how China incubates its tech startups. China's tech startup ecosystem presents two puzzling legal arrangements for human capital in light of Silicon Valley's experience, the co-existence of enforceable non-compete agreements and the high-velocity labour market, and the common use of stock options with a buyback norm. This article delves into the peculiarities of China's legal and political institutions to resolve these legal puzzles. This article also speaks to a global policy debate about the replicability of Silicon Valley and the necessity of such replication. The Chinese experience offers opposite examples showing the replication complexity: replication yet with deformed practices, and non-replication yet with similar outcomes. The findings suggest that there is unlikely to be a one-size-fits-all model for creating an innovation economy.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.385

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 communication0.0000.001
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 head0.041
GPT teacher head0.288
Teacher spread0.247 · 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