From Matchmakers to Innovation Machines: A Study of Market Generativity in the Bubble.io Ecosystem
Bibliographic record
Abstract
Many of the world’s most valuable companies (e.g. Google, Amazon, Apple, Alibaba), earn much of their income through marketplaces. Yet our understanding of marketplaces as intentionally designed products is hindered by the dominance of the matchmaking analogy, which implies that the main function of markets is allocation. We contend that marketplaces are often better viewed as distributed innovation machines, which is in line with definitions of generativity (Zittrain, 2006). In this abductive case study on the Bubble.io marketplace and its ecosystem, we investigate the process of intentionally leveraging generativity as a market design strategy and a source of advantage. We challenge the commonplace conceptions of generativity as an unintentional characteristic of technology, and markets as purely resource allocation systems. Our study introduces a novel framework for intentionally crafting a marketplace that is generative. Importantly our framework emphasizes that in order to succeed in generativity and benefit from it, the marketplace must also have good allocation and appropriation mechanisms.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".