A Consensus Against False Dichotomies in Crowdfunding?
Bibliographic record
Abstract
ABSTRACT Our opening pillar article for this special issue argued that the disruptive benefits of digital crowdfunding have been overstated. Responses to our piece draw on diverse cases and perspectives, inviting the field to both broaden its theoretical scope and expand its appreciation of crowdfunding's wide‐ranging empirical manifestations. The exchange also surfaces valuable insights about the current state of crowdfunding scholarship, suggesting a consensus against understanding crowdfunding in terms of simple dichotomies, like good versus bad or oppressive versus liberating, while revealing generative frictions among scholars' approaches. We identify three unresolved questions about the nature of crowdfunding that emerge from the responses: What is crowdfunding? What does crowdfunding do? How is crowdfunding actually practiced? Answering these questions requires confronting essential debates in the literature, including about crowdfunding's relationship to autonomy, resistance, and structural change. In search of these answers, the responses highlight the need for expanded empirical research into additional aspects of the phenomenon across different contexts, including the elite networks shaping crowdfunding platforms and ordinary users' perspectives on agency and resistance. Overall, the dialog calls for future research that develops the field's critical edge while appreciating the diversity of practices that crowdfunding involves.
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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 | 0.076 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.092 |
| Scholarly communication | 0.025 | 0.041 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.016 | 0.017 |
| 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".