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Record W2160040232 · doi:10.1111/etap.12049

Bifurcating Time: How Entrepreneurs Reconcile the Paradoxical Demands of the Job

2013· article· en· W2160040232 on OpenAlexaff
Danny Miller, Cyrille Sardais

Bibliographic record

VenueEntrepreneurship Theory and Practice · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsHEC MontréalUniversity of Alberta
Fundersnot available
KeywordsMindsetFace (sociological concept)Order (exchange)Quality (philosophy)Persistence (discontinuity)Frame (networking)ConfidentialityEconomicsBusinessPositive economicsMarketingPublic relationsLaw and economicsSociologyComputer scienceEpistemologyPolitical scienceComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

Entrepreneurs have been portrayed in paradoxically contrasting ways in the literature. On the one hand, they are said to be intrepid optimists who venture forth with great persistence even in the face of considerable uncertainty and multiple failures; on the other hand, they are held to be realists who are quick to acknowledge the negative realities of their initiatives and adapt very quickly. In order to reconcile these contrasting views, this study tracks in real time the frequent confidential communications of an entrepreneur and his closest consultant and partner during the last 6 months of a failing venture. We are able to gain insight into how by adopting a positive “frame” or consistent mindset about the future, the entrepreneur is able to sustain confidence in the face of significant challenge while at the same time acknowledging and reacting to significant problems in the present. We propose that an intrinsic quality of an entrepreneur is this ability to integrate or reconcile these seeming opposites—to manage paradox, largely by bifurcating time—by making temporal distinctions, and we show how that person simultaneously can be optimistic and realistic, and persistent and adaptive.

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 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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations39
Published2013
Admission routes1
Has abstractyes

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