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Record W7132629091

Intel? GrowthX: Partnering with Entrepreneurs for Growth

2022· other· en· W7132629091 on OpenAlex
Shameen Prashantham, Zhijing Cao

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

VenueCEIBS Institutional Repository · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsCentre Casa
Fundersnot available
KeywordsIntrapreneurshipLeverage (statistics)EntrepreneurshipProcess (computing)General partnershipValue (mathematics)Work (physics)
DOInot available

Abstract

fetched live from OpenAlex

The case describes the process of Intel promoting an intrapreneurship program in China. The intrapreneurship platform called Ideas2Realty (later rebranded as GrowthX) was founded in 2015. After five years’ development, GrowthX was improved step by step by introducing established entrepreneurship methodologies (e.g., the Lean Startup framework), partnering with an innovation accelerator, and inviting external startups. By the end of 2020, GrowthX had explored more than 400 business ideas, bringing Intel millions of dollars in incremental revenue. Why do companies like Intel need intrapreneurship? What challenges did Intel China overcome during the process? Going forward, for project leader Kapil Kane, several challenges remained. For example, how to leverage internal teams and external startups? How to achieve better synergies between different innovation programs? How to convince managers and employees that the program is of high long-term value and ask for more resources to develop?

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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