Hypothesis
  • The Hypothesis White Paper
  • Introduction
    • Thesis
  • The Hypothesis Vision
    • Defining "Trust" in a "Post-Truth" World
    • A New Way To Interact With Media
    • Interweaving Incentives With Foresight and Usefulness
    • A New Model of Accountability
    • Section Summary
  • Building a platform based on openness & transparency
    • Redefining “Community-Powered”
      • Formulating Questions
      • Context
      • Question Resolution
      • Groups
    • Data-Driven Gamification
      • Foresight
      • Usefulness
      • Quality of Research
      • Knowledge Metric
    • Protecting User Privacy
  • Monetization
    • A Note on Monetization
    • Subscriptions
    • Foresight API & the future of Hypothesis
      • Cultivating Oracles & User-Earned Revenue
      • The Media’s Monetization Problem
  • Concerns
    • Platform Limitations
    • Dangers of Bias
  • Conclusion
    • Closing Remarks
    • Roadmap
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  1. Building a platform based on openness & transparency

Redefining “Community-Powered”

We believe in the power of Hypothesis’ community to uphold and execute the core tenets of the platform. While such a notion is not uncommon among traditional social media models, open source communities and the increasingly popular philosophies behind blockchain-driven decentralization, Hypothesis sees community as something to be fostered within the context of accountability and transparency. In other words, the incentives and gamification structures outlined throughout this paper purposefully interweave the motivation of institution and individual to the extent that they rely on each other to meaningfully achieve their goals.

Hypothesis’ methods of socialization, while straightforward, are structured in a way such that interacting with other users on the platform most likely begets a constructive, friendly and competitively-oriented experience; one that is non-polarizing and, within the context of the platform’s gamification strategies, encourages users to step outside of their information bubble.

Those methods revolve around Hypothesis’ two primary formats of user-generated content: questions and context—both of which can be produced individually or through the collaboration of a group. Combined with follower networks, Hypothesis’ social incentives encourage users to perform at higher levels in pursuit of a more meaningful in-app experience.

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