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Early-Stage Entrepreneurial Action in Makerspaces: The Habitats, The Inhabitants, The Social Impact

2024· article· en· W4400440659 on OpenAlexaff
Letizia Mortara, Russell E. Browder, Howard E. Aldrich, Simon Ford, Tiantian Li, Valeria Dammicco, Ludmila Striukova, Riccardo Fini

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsStage (stratigraphy)Action (physics)HabitatEcologyBiology

Abstract

fetched live from OpenAlex

This presenter symposium explores the role of Makerspaces in fostering entrepreneurial innovation, particularly focusing on how these shared environments, where individuals can experiment and quickly learn to innovate, may act as catalysts for entrepreneurial activities. The presenter symposium addresses a gap in examining the prospective societal impact of these habitats, how they are evolving, and how their inhabitants behave in the early stages of entrepreneurial action. Key contributions of the symposium include the exploration of how Makerspaces function as entrepreneurship support organizations, the analysis of the Makerspaces population in the US, the role of Makerspaces in fostering inclusivity and diversity, the impact of Makerspaces on the identity evolution of venture founders and the details the stage-processes which transform innovators in nascent entrepreneurs. Overall, the symposium sets out to theorize and provide empirical evidence on how increased participation in Makerspaces can lead to more individuals engaging in early stages of entrepreneurship and how this could have positive societal impacts, aligning with the broader theme of 'Innovating for the Future'. Effectual entrepreneurship support: Hybrid resource mobilisation in Makerspaces Author: Russell E. Browder; U. of Oklahoma The Evolving Population of Makerspaces in the United States Author: Howard Aldrich; U. of North Carolina Underrepresentation in Makerspaces: Consequences for Entrepreneurship Author: Simon Ford; Beedie School of Business Simon Fraser U. Discovering Oneself in the Company of Others: The Role of Shared Social Spaces for Founders Identity Author: Tiantian Li; U. of Stuttgart Author: Ferran Giones; U. of Stuttgart From Innovation to Fledgeling Firms : Entrepreneurial Innovation Processes in Makerspaces Author: Valeria Dammicco; CRG Ecole Polytechnique Author: Letizia Mortara; Institute for Manufacturing, Engineering Department, U. of Cambridge

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0120.009
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.062
GPT teacher head0.365
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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