From Idea to Impact: Exploring the Development of Social Enterprises During the Crowd-funding Process
Bibliographic record
Abstract
Crowdfunding for sustainable and social enterprises has gained increasingly scholarly interest since 2013 (Böckel, Hörisch, & Tenner, 2021; Hussain, Di Pietro, & Rosati, 2023). Researchers have studied the factors that impact the success of a crowdfunding campaign. Still, little is known about how crowdfunding affects the organizational dynamics of nascent social enter-prises. The way in which crowdfunding can accelerate organizational communication and force nascent social enterprises to clarify their social mission, describe the social problem they are addressing, and speak in the name of their beneficiaries, values, and further figures has not yet been studied. To answer the research question of how social enterprises evolve during a crowdfunding cam-paign, the study will use a ventriloquial analysis of communicative elements in online commu-nication and interviews. This will help explore the dynamic of unfolding social entrepreneurial communication. The study aims to contribute to the social entrepreneurship literature by shed-ding light on how nascent social enterprises act under the pressure of fast and frequent public communication and how their organizational structures evolve. Additionally, the study will question the assumption that crowdfunding provides nascent social enterprises with sufficient start-up funding and identify the non-financial benefits of crowd-funding. It will also explore the interplay between public and private communication, the active role of platforms, and algorithms as other-than-human actors. The study will contribute to the theoretical and methodological discussion by introducing the Montreal School in Communica-tive Constitution of Organization (CCO) and ventriloquial analysis to social entrepreneurship research.
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 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".