Skill UI

Provides a comprehensive guide for designing rigorous academic studies for high-impact journals. Whether planning multi-study behavioral experiments, interpretive Consumer Culture Theory (CCT) fieldwork, or mixed designs, this resource ensures that the evidence collected is optimally structured to support the conceptual claim, emphasizing internal validity and transparency.

This comprehensive skill guides authors through the specific submission requirements and editorial culture of the Journal of Machine Learning Research (JMLR). It helps authors determine if their ML method, theory paper, or dataset is appropriate for JMLR's high standards—emphasizing rigor, complete proofs, open code, and lasting scientific contribution over novelty signaling. Use this when targeting JMLR or comparing it against other top-tier ML venues.