๐ง๐ต๐ฒ ๐๐ผ๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ: ๐๐๐๐ผ๐บ๐ฎ๐๐ฒ๐ฑ ๐ฅ๐ฒ๐๐ถ๐ฒ๐๐ฒ๐ฟ ๐ ๐ฎ๐๐ฐ๐ต๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐๐
You waste days finding reviewers. Your manual matching is slow. It often misses the right fit.
Use a point system. It balances three things.
- Topical Resonance: expertise match.
- Methodological Fitness: method match.
- Logistical Fitness: availability and past work.
Score these 40, 30, and 30. This equals 100. Conflict of interest gives a -100 penalty.
Use Google Cloud Natural Language API. Send the abstract to it. It gives you a list of themes and methods.
You have a paper on youth identity. The AI finds the method is qualitative interviewing. It matches Dr. Lee. She has those exact skills. She tops your list.
- Extract themes using your AI tool.
- Score reviewers based on the three pillars.
- Send a ranked list to your dashboard.
Automation removes the chore. It makes your process clear. You save time and reduce bias.
Source: https://dev.to/ken_deng_ai/the-core-engine-designing-your-automated-peer-reviewer-matching-system-with-ai-3mpe Optional learning community: https://t.me/GyaanSetuAi