A Theory of Digital Firm-Designed Markets: Defying Knowledge Constraints with Crowds and Marketplaces
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate the ways in which new forms of organization enabled by digital technologies, such as crowdsourcing and digital marketplaces, are allowing firms to circumvent and defy traditional knowledge constraints. This is part of the broader question of when and why these forms of organization are more efficient relative to alternatives, given that some firms simultaneously utilize crowdsourcing, marketplaces, and traditional forms of organization. We observe that an important cluster of these new organizational forms are able to circumvent knowledge constraints, because they combine elements of market and hierarchical organization in firm-designed hybrid arrangements. We further categorize these firm-designed markets into one-sided market arrangements (crowds) and two-sided market arrangements (marketplaces). To explain their efficiency relative to hierarchies and relative to each other, we take a knowledge-based perspective and review ways in which firm-designed markets reduce or remove both first-order (known unknown) and second-order (unknown unknown) knowledge constraints compared with hierarchies. Our argument hinges on the notion that firm-designed markets provide semidirected and undirected search and generativity mechanisms that allow firms to go beyond what is possible with centrally directed search.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it