EXPRESS: Hybrid governance of digital platforms: Exploring complementarities and tensions in the governance of peer relationships
Bibliographic record
Abstract
How do platforms integrate governance mechanisms that promote inherently distinct rules and incentives to manage peer relationships?Digital platforms combine market and community-based mechanisms to govern peer-to-peer interactions for value creation.However, these governance mechanisms play unique roles and interact in distinctive ways, thus shaping how platforms can leverage them for the governance of peer relationships.Through an analysis of sharing platforms, we identify under what conditions particular couplings of market and community mechanisms facilitate a stable governance configuration.We uncover how platforms leverage complementarities and avoid tensions among a set of core and elaborating governance mechanisms.The findings show that market and community mechanisms and their interactions constrain platform governance in different ways.When platforms have strong commercial identities and offerings, implement strict assurance instruments, or develop strong social institutions, they confine core mechanisms to a single governance structure and prevent innovative configurations.However, under specific conditions, platforms explore complementarities between market and community mechanisms which lead to either mixed or highly mixed governance configurations.The study uncovers platforms' possibilities and constraints in developing stable governance configurations which hybridize market and community 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".