We have the selection of the best community contributors from Deep Funding Round 4 (DFR4)! This recognition is a testament to the dedication, expertise, and collaborative spirit that drive our community. Here’s an inside look at how we determined the top contributors, ensuring a fair and comprehensive evaluation process.
Our Approach
The selection process was meticulously designed to balance quantitative metrics with qualitative assessments. We implemented a two-phase approach:
- Interaction Scoring Phase:
- We revamped the interaction scoring method from previous rounds, incorporating community feedback and platform changes.
- The new interaction score is calculated by multiplying the difference between upvotes and downvotes by the total number of reviews made by each community member.
- Interaction Score=(Upvotes−Downvotes)×Total Number of Reviews\text{Interaction Score} = (\text{Upvotes} – \text{Downvotes}) \times \text{Total Number of Reviews}Interaction Score=(Upvotes−Downvotes)×Total Number of Reviews
- Based on this interaction score, we shortlisted community members who performed exceptionally well.
- Human Review Phase:
- For each shortlisted member, we randomly selected five reviews and evaluated them by independent Review Circle reviewers.
- These reviews were scored on a scale from 0 to 3, focusing on relevance and quality.
- The average score from these evaluations determined the final ranking of the top 10 contributors.
Data and Analysis
We started with a comprehensive dataset, including various metrics such as review summaries, ratings, upvotes, downvotes, and the number of replies. From this data, we derived the interaction scores and identified trends and correlations:
- Interaction Score Trends:
- The distribution of interaction scores followed an exponential trend, correlating highly with the number of reviews and the upvote-downvote difference. This confirmed the interaction score as a reliable metric.
- Human Review Insights:
- The final grades from the human review phase followed a normal distribution, with mean grades slightly higher for non-experts than experts.
- Minimal bias was observed in the rating patterns, indicating a fair and balanced grading process.
- Correlation Analysis:
- We found a low Pearson correlation coefficient (0.207) between interaction scores and final grades, suggesting these metrics measure different aspects of contribution.
- This low correlation supports the robustness of our multi-faceted approach, ensuring a comprehensive evaluation of community members.
Key Findings
- Combining quantitative interaction scores and qualitative human reviews provided a balanced and fair method for identifying top contributors.
- The interaction score effectively captured quantitative engagement metrics, while human reviews offered qualitative insights into the relevance and quality of contributions.
- The complementary nature of these metrics ensured a well-rounded evaluation, highlighting the top contributors who significantly impacted the community.
Top Contributors
Here are the top 10 contributors from DFR4 along with their final scores:
- Contributor A – Score: 2.86
- Contributor B – Score: 2.65
- Contributor C – Score: 2.50 (Expert Reviewer)
- Contributor D – Score: 2.34
- Contributor E – Score: 2.20
- Contributor F – Score: 2.10
- Contributor G – Score: 2.00 (Expert Reviewer)
- Contributor H – Score: 1.95
- Contributor I – Score: 1.90
- Contributor J – Score: 1.85 (Expert Reviewer)
Please note that the three expert reviewers listed above will not receive additional payment or merchandise as awards, as they have already been rewarded for their expert review process.
Conclusion
Our approach to selecting the best contributors in DFR4 underscores our commitment to fairness, transparency, and continuous improvement. We are proud to recognize these outstanding community members and look forward to further enhancing our evaluation processes in future rounds.
We extend our heartfelt congratulations to the top contributors and thank the entire community for their ongoing support and dedication. Together, we continue to build a thriving, innovative, and collaborative ecosystem.
Special thanks to the Operations Circle coordinators, Rojo, who performed the calculations and developed the algorithm behind this selection process, and Victor (MayorDeFi), who supported the process and provided the data. Another special thanks to the reviewers from the Review Circle who contributed tremendously into going through contributions in a short interval. Your efforts were crucial in making this possible.
For more detailed insights and data analysis, you can refer to the full report here.
You can also view the detailed spreadsheet with the contributors and their scores here.
Stay tuned for more updates and join us in celebrating the achievements of our exceptional community!
Thank you for being a part of our journey!
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