Skill UI

This guide provides crucial advice for authors submitting to conferences with severe page limitations, such as DAC. It teaches the 'decision-criticality test' to help authors allocate content, ensuring that only core, review-mandated arguments remain in the main body, while supporting details and code can be moved to external repositories.

This guide assists users in structuring and rigorously defending the methodological design for demographic manuscripts. It provides detailed guidance on selecting and justifying advanced techniques—including life tables, decomposition analysis, survival models, Age-Period-Cohort (APC) modeling, and microsimulation. It emphasizes critical steps like establishing identification assumptions, addressing causal biases (selection and exposure), and formulating an adjudication test to rule out rival explanations, ensuring the research is methodologically robust.

This tool provides a comprehensive guide to the manuscript review process for Demography journal. It details the double-blind review structure, the mandatory pre-review desk screening (by Editor/Deputy Editor), and the common grounds for desk rejection. By stress-testing your paper against these academic standards, authors can significantly improve their work, ensuring methodological rigor and general interest before submission.

This skill guides users to transform mere empirical findings or correlations into robust, publishable academic contributions within the field of demography. It teaches how to structure claims by identifying explicit mechanisms, scope conditions, and observable implications, thereby satisfying the academic requirement that results must be tied to a testable population-science mechanism, moving beyond simple description.

This skill provides a comprehensive framework for assessing whether a research project meets the academic scope of general population science. It guides users to shift focus from mere data usage to advancing core demographic questions concerning fertility, mortality, migration, and population change, helping to frame the contribution for maximum impact.

A comprehensive guide for authors responding to Developmental Psychology journal revise-and-resubmit (R&R) critiques. It provides a structured strategy to address rigorous demands—such as measurement invariance testing, attrition analyses, and enhanced transparency—while strengthening the developmental claim and managing the manuscript's length constraints.

This guide details the rigorous review standards and criteria for developmental psychology manuscripts, covering masked peer review, developmental significance, methodological rigor (age/cohort effects, invariance, attrition), JARS reporting, and TOP transparency. Use this resource to stress-test your paper before submission, understand expected revisions, or interpret complex decision letters.

This comprehensive guide helps researchers determine if their empirical project qualifies for a Developmental Psychology journal. It emphasizes that the core contribution must focus on 'developmental change' across the lifespan (age effects, trajectories, or underlying mechanisms), rather than merely describing a single-age phenomenon. It provides a four-gate fit test covering developmental claims, theory advancement, design credibility, and open-science readiness, guiding researchers on framing and proper submission tiering.

A comprehensive guide for structuring the empirical evidence section of top-tier NLP conference papers (e.g., EACL). It mandates rigorous controls, including fair baselines (tuned models, explicit LLM prompts), statistical significance testing (variance, CI), contamination checks, and detailed human evaluation protocols. Use this to elevate your research from mere assertion to scientifically audited proof.

This comprehensive checklist is designed for auditing NLP research papers, particularly for major conferences like EACL. It serves as a claims audit, ensuring that every reported result, claim, or finding—including hyperparameters, compute costs, verbatim prompts, data sources, and significance testing—is fully documented and reproducible. It emphasizes academic rigor and transparency in AI research.

This guide helps researchers determine the optimal academic conference (ECAI, IJCAI, AAAI, etc.) for their AI contribution *before* starting to write. It provides structured tests (like the Model-swap test and General-audience test) and criteria (contribution shape, main track vs. application) to ensure the work is optimally matched to a specialized venue, maximizing the chances of acceptance and impact.

A comprehensive guide for designing and auditing high-stakes computer vision experiments for top-tier conferences. It covers critical areas like 'matched-substrate fairness,' 'six-month-staleness testing,' and rigorous ablation studies, ensuring that claims are robust, reproducible, and withstand intense peer review scrutiny.