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Record W1961678269 · doi:10.5430/bmr.v4n2p13

Research on the Problems and Countermeasures of Chinese Automobile Enterprises Transnational Merger and Acquisition

2015· article· en· W1961678269 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBusiness and Management Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Political and Economic Relations
Canadian institutionsnot available
FundersPeople's Government of Jilin Province
KeywordsBusinessPoliticsProcess (computing)Ideal (ethics)Mergers and acquisitionsAutomotive industryIndustrial organizationEconomic systemFinanceEconomicsLawComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

In recently years, affected by the international and domestic economic environment, Chinese automobile enterprises get really perfect time to implement cross-border mergers and acquisitions, so we can see that some Chinese automobile enterprises have already done it. At the same time we should also know there are lots of risks and problems in the process of transnational M&A, Such as political barriers, legal barriers and cultural conflicts and so on, which often do not make ideal results when they implement the M&A. At beginning of this paper will talk about the environment of Chinese automobile enterprises transnational M&A. And then on the basis of analyzing the current situation and problems of Chinese automobile enterprises transnational M&A, this paper will put forward some suggests which are appropriate for Chinese automobile enterprises.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.152
GPT teacher head0.407
Teacher spread0.255 · 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