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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".