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Record W4311350786 · doi:10.1177/08997640221139430

The Emergence and Evolution of Digital Social Ventures in Dublin, Ireland

2022· article· en· W4311350786 on OpenAlexaff
Sheila Cannon, Raymond A. Dart

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsTrent University
Fundersnot available
KeywordsSituatedProcess (computing)Social entrepreneurshipPhenomenonBusinessSociologyKnowledge managementEntrepreneurshipPublic relationsPolitical scienceComputer scienceEpistemology

Abstract

fetched live from OpenAlex

Digital social ventures are initiatives that intend to transformatively engage social and environmental problems through the application of digital technology and are a new phenomenon found globally. While the broad influence and consequences of disruptive digital technology are increasingly taken for granted, very little research focuses on the deliberate use of digital technology for social purpose. This study is situated within three areas of literature: disruption caused by digital innovations, the use of digital technology by social purpose organizations, and social entrepreneurship. The process of digital social venture emergence and evolution shows how flashy-sounding technological solutions develop into more modest and incrementally useful tech-supported adjuncts. A preliminary framework for conceptualizing the nature and process of digital social ventures shows how a Schumpeterian approach to social entrepreneurship as disrupting equilibrium gives way to a Hayekian approach as drawing on local, embedded knowledge to achieve incremental change.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.009
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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