Early-Stage Entrepreneurial Action in Makerspaces: The Habitats, The Inhabitants, The Social Impact
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".