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Record W3037226056 · doi:10.1287/mnsc.2020.3920

Enabling Entrepreneurial Choice

2021· article· en· W3037226056 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

VenueManagement Science · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConflationQuality (philosophy)Face (sociological concept)Test (biology)EconomicsMarketingComputer sciencePositive economicsMicroeconomicsBusinessSociologyEpistemology

Abstract

fetched live from OpenAlex

Entrepreneurs must choose between alternative strategies for bringing their idea to market. They face uncertainty regarding both the quality of their idea as well as the efficacy of each strategy. Although entrepreneurs can reduce this uncertainty by conducting tests, any single test conflates the signal of the efficacy of the particular strategy and the quality of the idea. Resolving this conflation requires exploring multiple strategies. Consequently, entrepreneurial choice is enhanced by finding ways to lower the cost of testing multiple strategies, receiving guidance as to the types of tests likely to reduce signal conflation, and optimally sequencing tests based on previous beliefs. This creates a role for judgment that may be provided by trusted third parties such as mentors and investors. We hypothesize that institutions that lower the cost of transmitting and aggregating judgment spur entrepreneurial performance. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
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 communication0.0010.002
Open science0.0010.001
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 head0.019
GPT teacher head0.238
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