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Corporate Ventures and Knowledge

2015· other· en· W1571412262 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

VenueWiley Encyclopedia of Management · 2015
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKnowledge managementDynamic capabilitiesBusinessKey (lock)Knowledge baseSoftware deploymentKnowledge economySelection (genetic algorithm)Knowledge value chainKnowledge creationOrganizational learningComputer scienceMarketingArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The role of knowledge, the different types of knowledge, and the processes of searching for and learning new knowledge are all critical components in understanding successful corporate venturing (CV). This article examines some of the recent insights into each of these areas and how they tie to the entrepreneurial activities of established firms. In particular, it links the firm's knowledge base to dynamic capabilities. This approach highlights the following key relationships between knowledge and dynamic capabilities: (i) new knowledge is one of the key outcomes of a firm's dynamic capabilities; (ii) firms produce new knowledge in part through specific dynamic capabilities for external knowledge search and selection, and (iii) firms produce new knowledge in part through specific dynamic capabilities for internal knowledge deployment and knowledge reconfigurations.

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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.226
Teacher spread0.206 · 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