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Record W2020870619 · doi:10.1177/0266242607076528

Entrepreneurial Social Capital Unplugged

2007· article· en· W2020870619 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

VenueInternational Small Business Journal Researching Entrepreneurship · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsBishop's University
Fundersnot available
KeywordsSocial capitalAmbiguityReciprocalAsset (computer security)Social reproductionCapital (architecture)Social network (sociolinguistics)Work (physics)BusinessSocial mobilityMicroeconomicsEconomicsSociologyComputer scienceSocial mediaEngineeringComputer security

Abstract

fetched live from OpenAlex

First, we develop a macro level explanation for how changes in social capital occur. Second, we apply a dynamic approach to social capital by examining on a more micro level the activities which are involved with and contribute to the increase or decrease in social capital in, largely dyadic, relationships within entrepreneurial networks.Third, we propose a more robust notion of social capital.We found that five key relationship-driving forces were involved in developing social capital.The research findings suggested that entrepreneurs seem to have a strikingly similar modus operandi when it comes to creating and destroying their network social capital. Activities such as the reciprocal trading of favours, socializing, joint problem-solving, delivering to expectation and using transparent communications were crucial to both. Finally, we discovered that entrepreneurs managed their network relationships heuristically relying on social capital, particularly by leveraging the productive ambiguity of this crucial [net]work asset.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.064
GPT teacher head0.365
Teacher spread0.301 · 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