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Record W4200546209 · doi:10.5539/jpl.v15n1p40

Implications of the Digital Economy on Merger Control in Pakistan and China: Policy Implications for Pakistan

2021· article· en· W4200546209 on OpenAlexvenueno aff
Yuhui Wang, Shahzada Aamir Mushtaq

Bibliographic record

VenueJournal of Politics and Law · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsnot available
FundersHenan University
KeywordsDigital economyCompetition (biology)ChinaEconomicsCurrencyEmerging marketsEconomyMarket economyInternational tradeBusinessPolitical scienceLawMacroeconomics

Abstract

fetched live from OpenAlex

The rise of the digital economy has challenged the foundation of competition law frameworks the world over. Today, the antitrust doctrine finds itself confronting a new economy; an econo-my wherein data acts as a currency, markets are without prices, market collisions are based on algorithms, and the market is ‘infinite’. Several jurisdictions such as Germany, Austria, and China have developed new regulations or amended existing legislations to confront the chal-lenges presented by the digital economy. A dearth of theoretical and empirical literature has evaluated whether digital markets are so fundamentally different as to require a different set of rules. Of specific interest to this paper is whether current competition rules are sufficient to deal with mergers and acquisitions (M&As) in digital markets. This paper assesses M&A regulations in China and Pakistan in light of the new digital economy. Expert interviews were conducted using semi-structured interviews to investigate the comparisons between Pakistan’s and China’s merger control regimes. The findings indicate that China’s merger control regulations are better adopted for the digital economy than Pakistani’s. It also sets out the policy implications for competition policy makers in Pakistan.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.367

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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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 designTheoretical or conceptual
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

Citations3
Published2021
Admission routes1
Has abstractyes

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