Chapter 6: Contribution Recognition Mechanism
6.1 The Core Challenge of Recognition
Contribution recognition is the most critical and most difficult component of GMC. The core challenge lies in:
- Contributions can be objective (quantifiable) or subjective (requiring evaluation)
- Objective measurement is naturally resistant to fraud but has narrow coverage
- Subjective evaluation has broad coverage but is easily manipulated (similar to fake online reviews)
6.2 Two Acquisition Methods
Method 1: Objective Measurement
Based on verifiable objective metrics, the system automatically mints Merit:
| Measurement Dimension | Examples | Characteristics |
|---|---|---|
| By volume | Customers served, proposals delivered | Auditable, fraud-resistant |
| By time | Service hours, online duration | Timestamps are verifiable |
| By output | Code commits, documentation produced | Traceable on-chain |
Advantages: Automatic, efficient, high difficulty of fraud. Limitations: Cannot cover all types of contributions.
Method 2: Task Bounty
Preset Merit for a specific task; upon completion, stakeholders vote to confirm:
- Publish: Define the task objective, Merit reward, and influence duration
- Execute: The executor completes the task and submits results
- Vote: Stakeholders vote on whether the criteria are met
- Mint: Upon approval, the system mints MeriToken
6.3 Stakeholder Mechanism
Who Are the Stakeholders
Parties with a vested interest in a given contribution. For example:
- A government consultation coFay's contribution → voted on collectively by its users
- An open-source project contribution → voted on by the project's users and collaborators
Key Rule: Exclude High-Intimacy Individuals
Since GMC records the social relationship network, the system can:
- Identify individuals whose intimacy with the contributor exceeds a threshold
- Exclude these individuals from the voting pool
- Select voters from the remaining stakeholders
This is the core mechanism for preventing "insiders voting for insiders."
Consensus Approval Conditions
- A proportion threshold is set (e.g., 2/3 majority)
- Voting weight is tied to the voter's own MeriToken
- Once the threshold is exceeded, the system automatically mints
6.4 Determining Influence Duration
Each contribution recognition must also determine the influence duration:
| Determination Method | Applicable Scenario |
|---|---|
| Preset by contribution type | Objective measurement (e.g., customer service interaction = 30 days) |
| Set by task publisher | Task bounty |
| Decided collectively by voters | Community consensus |
The influence duration determines the decay rate of that Merit batch.
6.5 Anti-Fraud Strategies
Core question under discussion: Bitcoin mining is purely objective measurement, naturally fraud-resistant. But GMC includes subjective evaluation — how do we prevent fake reviews?
Approach: Not to eliminate subjectivity, but to make the cost of fraud far exceed the benefit.
Defense combination:
- Intimacy exclusion: Exclude voters with close relationships to the subject being evaluated
- MeriToken weighting: High-reputation voters carry more weight; fraudsters must first accumulate substantial genuine reputation
- Voting behavior audit: Frequently voting in favor of a specific subject → flagged as anomalous
- Random sampling: Randomly select voters from the stakeholder pool to reduce the possibility of collusion
- Retroactive accountability: If fraud is discovered, it can be addressed retroactively through the penalty mechanism
Design Principle
Decompose contributions into objectively measurable components as much as possible, reducing the proportion of subjective evaluation:
- Prioritize objective measurement (automatic, efficient, fraud-resistant)
- Subjective evaluation is used only for scenarios that cannot be objectively quantified
- Subjective evaluation employs multiple layers of defense to reduce fraud risk
6.6 Discussion Notes
Design trade-offs in contribution recognition:
- Efficiency vs. fairness: Objective measurement is efficient but narrow; subjective evaluation is comprehensive but susceptible to manipulation
- Participation vs. quality: Lowering the voting threshold increases participation but may reduce evaluation quality
- Current approach: "Objective first + subjective supplement + multi-layered defense"
- Extended question: How is Merit created from nothing? → See the Economic Model chapter