Skill UI

A comprehensive guide for authors to sharpen the marginal contribution of their Experimental Economics (ExpEcon) manuscripts. It moves beyond simple topic novelty by outlining a structured ladder of contribution (e.g., confound removal, new decisive treatment) and teaching authors how to precisely map their design against canonical behavioral paradigms and prior work. Essential for improving academic rigor and manuscript acceptance rates.

This guide provides rigorous guidelines for designing and presenting results in Experimental Economics manuscripts. It emphasizes making the treatment comparison and its uncertainty immediately visible through specific figure types (distributions, dynamics plots) and structured tables, ensuring the findings are self-contained and highly credible for academic review.

A structured guide on translating behavioral or game-theoretic arguments into pre-specified, quantitative, and testable hypotheses for Experimental Economics (ExpEcon) manuscripts. This methodology emphasizes moving beyond informal intuition by defining the experimental game, deriving benchmark predictions, layering competing behavioral accounts, and generating comparative-statics hypotheses that treatments must adjudicate. It is essential for manuscripts aiming to robustly test theory through design.

This guide helps researchers structure their economic questions for top experimental economics journals. It emphasizes framing the contribution around a novel, clean experimental design (the 'minimal pair') and the treatment effect, rather than merely the topic itself. It guides authors on decision-making regarding experimental types, pre-registration, and selecting the appropriate academic venue.

This router guides researchers through the systematic process of preparing a manuscript for Experimental Economics (ExpEcon), the ESA's flagship journal. It identifies the correct specialized skills (e.g., theory modeling, robustness checks, referee strategy, rebuttal) needed at each stage, ensuring adherence to methodological rigor, including incentive compatibility and the no-deception norm. It is designed to help teams structure their research from initial design to final submission.

This comprehensive guide provides structural and stylistic advice for authors submitting experimental economics manuscripts. It emphasizes adopting a 'design-forward' narrative, ensuring procedural transparency, quantifying results in subjects' units, and meticulously structuring the core design section to meet the high standards of expert reviewers.

This tool guides authors through the rigorous standards required for publishing in top experimental economics journals. It serves as a comprehensive checklist, evaluating a manuscript's fit, methodological strength, design transparency, and structural requirements (e.g., pre-registration, incentive-compatibility) against best practices in behavioral and experimental economics.

This guide outlines the rigorous methodological standards for empirical work in AI fairness, accountability, and transparency (FAccT). It advises researchers on matching specific claims (e.g., disparate harm) to appropriate evidence types—including disaggregated metrics, coded qualitative data, and documented ethical procedures. It stresses the importance of ethical review, handling protected attributes, and acknowledging limitations to ensure high scientific and ethical rigor.

A comprehensive methodology for designing scientific and reproducible storage evaluations, particularly for high-stakes submissions. This guide covers controlling device state (aging, preconditioning, TRIM), selecting credible workloads (fio, YCSB), matching metrics (P99.9 latency, bytes-written) to claims, and rigorously testing durability and crash consistency on real hardware.

Provides rigorous guidance and structure for statistical analysis in Field Crops Research (FCR). Focuses on implementing linear mixed models that respect complex experimental designs (e.g., split-plot, block, environment). Essential for proper G×E analysis, robust means separation, and accurate evaluation of crop models, ensuring results are methodologically sound and actionable.

Provides essential guidelines for designing rigorous, generalizable field experiments in crop research. Covers multi-environment trials (MET), proper randomization, blocking strategies, split-plot designs, and Genotype-by-Environment (G×E) analysis. Use this tool when planning or defending the scientific rigor of an agricultural manuscript.

This guide provides a rigorous framework for designing and auditing empirical studies in software engineering (SE). It emphasizes that evidence must proportionally match the claims made, covering topics like using real subject systems, establishing fair baselines, calculating SE-standard statistics (effect sizes, CIs), and ensuring thorough provenance, especially for LLM-based work. It helps researchers strengthen their research methodology before publication.