Bibliographic record
Abstract
Abstract This article analyzes the geographic clustering of crowdfunding (CF) activity across two countries at the city level. We find that the ability of Kickstarter projects to attract funding or backers is spikier than the simple number of projects, suggesting that while the locations of Kickstarter projects are not as clustered, projects that are able to recruit funding are clustering. In addition, we find that digital media (DM) projects cluster more than Local projects. Yet, once we control for the pre-existing geographic distribution of population and economic activity, we find more complex patterns of geographic clustering. The spatial clustering of total Kickstarter funds raised is largely explained by the population and economic activity controls. Conditional on those controls, funds raised for DM projects do spatially cluster, while funds raised for Local projects exhibit significant dispersion. Funding and number of backers cluster for DM projects, above and beyond the prior concentration of socioeconomic and employment factors. Conversely, our results suggest CF can reduce or flatten the spikiness of fundraising for local projects. The world was already spiky, and it is a bit less so thanks to CF platforms like Kickstarter.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".