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Record W4362670333 · doi:10.54097/hbem.v7i.6973

Future Trend of Virtual Machines: A Case Analysis of Broadcom's Acquisition of VMware

2023· article· en· W4362670333 on OpenAlexaff
Haotian Lou

Bibliographic record

VenueHighlights in Business Economics and Management · 2023
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsEarl Haig Secondary School
Fundersnot available
KeywordsCloud computingBusinessStock (firearms)CashSoftwareRecessionProduct (mathematics)CommerceFinanceComputer scienceOperating systemEngineeringEconomics

Abstract

fetched live from OpenAlex

On May 26, Broadcom, the U.S. communications chip giant, announced that it will acquire cloud services provider VMware for $61 billion in a stock plus cash deal. Broadcom would assume $8 billion of VMware's debt under the terms of the agreement, while its Broadcom Software Group will continue to operate under the name VMware. In tough economic times, it is customary for large technology companies to double their bets: in the 1990s, the old AA transformed itself from a hardware company into a software and services company during the recession, and in the midst of the global economic downturn, Broadcom has made no secret of its intent to expand and grow its software business by accelerating its investments and acquisitions. If Broadcom and VMware can combine their strengths, not only will customers benefit from the integration of product portfolios, but the synergies between these different products could open up new business models.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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