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Record W2074526713 · doi:10.2307/41166358

Managing Creativity in Small Worlds

2006· article· en· W2074526713 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

VenueCalifornia Management Review · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsStraddleCreativitySilicon valleyBusinessSocial worldsKnowledge managementKnowledge transferOpen innovationMarketingIndustrial organizationEconomic geographySociologyEconomicsPolitical scienceComputer scienceSocial scienceEntrepreneurship

Abstract

fetched live from OpenAlex

Greater job mobility among engineers and scientists has caused the extended social networks of inventors to become increasingly connected. As a result, invention increasingly occurs within small worlds (or social networks) that straddle firm boundaries. Small worlds provide both strategic opportunity and potential threat; while they can increase creativity within a firm, they also aid in the diffusion of creative knowledge to other firms through personnel and knowledge transfer. Firms that operate within small worlds such as in Silicon Valley long agolearned to manage invention in an environment of rampant knowledge spillovers across firm boundaries. Now, however, all firms need to learn how to manage innovation in a small world environment. This article offers them advice about how to do this. (Publisher’s Abstract)

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)0.001
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.228
Teacher spread0.203 · 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