Skill UI

This tool guides authors writing on industrial organization, market structure, regulation, and antitrust. It provides a rigorous framework for evaluating whether a manuscript meets the high standards of top-tier IO journals by assessing theoretical models, empirical identification, and policy significance. Use it for venue selection and manuscript re-framing.

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

This guide outlines the rigorous standards for quantitative analysis required in academic economic research, particularly dynamic modeling. It provides a framework covering systematic model calibration, moment estimation, numerical solution discipline, and establishing fully reproducible computation workflows, ensuring results are highly auditable and verifiable.

This skill guides authors needing to assess the suitability of their research on resource efficiency, waste management, or recycling for the journal Resources, Conservation & Recycling. It provides detailed expectations regarding Material Flow Analysis (MFA), Substance Flow Analysis (SFA), and the necessity of presenting quantified, measurable circularity contributions, ensuring rigorous methodological alignment with the circular economy framework.

This guide provides a framework for selecting and defending the appropriate research methodology for innovation-studies claims. It covers best practices for patent/bibliometric analysis, causal policy evaluation (e.g., DID, RDD), survey design, case studies, and mixed methods. It emphasizes that the method must fit the construct, not merely the indicator, guiding users to defend the underlying theoretical mechanism.

This guide assists in building a robust theoretical framework for research policy manuscripts. It moves beyond simple empirical measurement by structuring the argument using the innovation-mechanism chain (antecedent → mechanism → outcome). It teaches how to connect firm-level findings up to the systemic policy or institutional level, ensuring the contribution is theoretically advanced and legible across disciplines (e.g., economic, managerial, STS).

A comprehensive framework designed for rigorous academic research, ensuring that primary findings remain stable across various methodological choices. It systematically tests the robustness of estimates against potential weaknesses in specification, sample selection, measurement, identification, and inference. This suite defends the headline result against skeptical scrutiny, which is crucial for publication in top-tier economics journals.

This guide provides a rigorous framework for stress-testing and implementing state-of-the-art causal identification strategies required for high-impact empirical economics. It covers advanced methodologies including Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Synthetic Control, and Structural Modeling. It emphasizes modern estimators (e.g., Callaway-Sant'Anna, effective F) and robust diagnostic checks to ensure the causal claims are methodologically sound and defensible.

This guide provides a comprehensive methodological framework for conducting and reporting rigorous robustness checks in high-stakes empirical economics papers. It details crucial steps—including specification stability, placebo tests, inference corrections, and mechanism evidence—necessary to anticipate skeptical reviewers. The goal is to harden findings, ensure reproducibility, and elevate the credibility of research findings.

This guide is designed for systematic reviews and meta-analyses, providing a rigorous framework to stress-test the robustness and completeness of reported findings. It mandates advanced checks such as saturation evidence, risk-of-bias appraisal, and statistical diagnostics (e.g., heterogeneity, publication bias) to ensure conclusions are not artifacts of omission or methodological weakness. Essential for high-level academic research.

This tool guides the transformation of a coded, screened corpus into a robust analytical spine for academic synthesis. It moves beyond simple annotated bibliography summaries by requiring the construction of a conceptual framework (e.g., taxonomy, theoretical model, tensions). This structure ensures the review advances theory or policy, providing a coherent, critical argument for the field.

Provides a structured framework for vetting potential review topics for high-impact educational journals. It guides researchers through four critical fit tests—educational importance, literature maturity, current need for synthesis, and scope tractability—before committing to a full search or writing a protocol, ensuring the proposal genuinely advances academic understanding.