We've been experimenting with using LLMs to help identify and prioritize research for Unjournal evaluation, to work with and complement human prioritization (and learn). We now have a public prototype dashboard. It's early stage and needs refinement we have not invested a lot of compute/API credit into this.
What it does: Automatically discovers recent papers from NBER, arXiv (econ), CEPR, SSRN, Semantic Scholar, EA Forum paper links, OpenAlex, Anthropic Research and then using AI models (mostly GPT-5.4 family) against our prioritization criteria— decision relevance, prominence, timing value, and methodological potential.
Domain: economics, quantitative social science, forecasting, and policy-relevant research
Caveats: This is very preliminary and the AI recommendations are not yet well-calibrated. As of 14 Apr 2026 many of the suggestions are mediocre we're sharing it for transparency and feedback. This supplements our existing Public Database of Prioritized Research on Coda (and those papers have been folded in here too. )
Scores reflect evaluation priority (expected value of commissioning an independent review), not research quality. ATM (IIRC) the AI only sees paper metadata and abstracts, not full texts. There's also a statistics page showing the breakdown by source, cause area, and score distribution.
Probably building towards a hybrid/centaur model here, with human and AI prioritization feedback reinforcing each other. (And see "planned workflow" at the bottom.)
Feedback encouraged. You can also comment directly on the page via Hypothes.is, and we'll adapt.
We've been experimenting with using LLMs to help identify and prioritize research for Unjournal evaluation, to work with and complement human prioritization (and learn). We now have a public prototype dashboard. It's early stage and needs refinement we have not invested a lot of compute/API credit into this.
What it does: Automatically discovers recent papers from NBER, arXiv (econ), CEPR, SSRN, Semantic Scholar, EA Forum paper links, OpenAlex, Anthropic Research and then using AI models (mostly GPT-5.4 family) against our prioritization criteria— decision relevance, prominence, timing value, and methodological potential.
Domain: economics, quantitative social science, forecasting, and policy-relevant research
Caveats: This is very preliminary and the AI recommendations are not yet well-calibrated. As of 14 Apr 2026 many of the suggestions are mediocre we're sharing it for transparency and feedback. This supplements our existing Public Database of Prioritized Research on Coda (and those papers have been folded in here too. )
Scores reflect evaluation priority (expected value of commissioning an independent review), not research quality. ATM (IIRC) the AI only sees paper metadata and abstracts, not full texts. There's also a statistics page showing the breakdown by source, cause area, and score distribution.
Probably building towards a hybrid/centaur model here, with human and AI prioritization feedback reinforcing each other. (And see "planned workflow" at the bottom.)
Feedback encouraged. You can also comment directly on the page via Hypothes.is, and we'll adapt.