Skill UI

A rigorous methodological framework for identifying core risk and uncertainty parameters in academic research. This guide details how to design experiments or structural models to explicitly separate confounds (e.g., utility curvature vs. probability weighting, preferences vs. beliefs) and ensure that the measured parameter is genuinely identified, thereby enhancing the scientific rigor of behavioral finance and econometrics studies.

A comprehensive guide for researchers developing models in decision theory under risk and uncertainty. It outlines how to strengthen theoretical representations by ensuring the model is anchored by clear axiomatic foundations, a mathematically tractable functional form, and crucially, verifiable behavioral content (i.e., what the model predicts or forbids). This helps bridge the gap between abstract theory and empirical data.

This guide provides rigorous structural and stylistic guidelines for academic manuscripts in the field of risk and uncertainty. It instructs authors on how to precisely articulate decision models, quantify results with appropriate magnitude and precision, and structure arguments (Abstract, Introduction, Exposition) to meet the high standards of specialized journals, ensuring technical honesty and clarity.

A comprehensive guide for developing rigorous spatial economic models, designed for manuscripts needing deep theoretical interpretation (e.g., JUE submissions). It instructs users on how to build quantitative spatial models (QSMs), sorting models, and spatial-equilibrium frameworks to interpret reduced-form estimates, establish counterfactual policies, and ensure that theory rigorously disciplines the empirical findings. Focus areas include general-equilibrium reallocation, policy invariance, and identifying parameters.

A comprehensive guide for advanced empirical research in open economies. It outlines best practices for establishing causal inference by anchoring identification strategies in external or institutional shocks (e.g., tariffs, trade agreements, exchange rate reforms). The guide emphasizes modern critiques of standard methods like shift-share and gravity models, providing detailed protocols for DID, Bartik decomposition, and IV estimation to meet top-tier academic journal standards.

This guide provides best practices for ensuring the scientific rigor and reproducibility of data mining and machine learning research published at top-tier conferences like KDD. It details a three-tier honesty model (Rerunnable, Rebuildable, Attested) and provides comprehensive checklists for disclosing pipeline stages, handling stochasticity, and documenting configurations to build reader trust.

This guide models the rigorous, multi-cycle decision-making process for top-tier data mining conferences like KDD. It details how decisions are weighed, considering reviewer scores, rebuttal strategies, area chair synthesis, and specific criteria (e.g., reproducibility, ethics, industrial feasibility) across outcomes like Accept, Resubmit, and Reject. Essential for understanding academic publishing rigor.

A comprehensive guide detailing how to effectively distribute content across the three components of a major conference submission: the core content pages, the optional appendix, and the cited external repository. It provides best practices for content triage, ensuring that only load-bearing, reviewer-visible claims are placed in the limited space, while supporting details are properly migrated or relegated to the repository.

A comprehensive guide on structuring high-impact medical abstracts, modeled after standards like The Lancet. Learn the mandatory five sections (Background, Methods, Findings, Interpretation, Funding), strict word limits, and crucial reporting requirements, such as effect sizes, 95% CIs, and trial registration numbers, to ensure your manuscript meets top-tier editorial standards.

Provides detailed guidelines for creating high-standard clinical figures and tables, modeled after leading medical journals (e.g., The Lancet). It covers essential display items including Table 1 (baseline characteristics), Kaplan-Meier survival curves (with numbers-at-risk), Forest plots for meta-analyses, CONSORT/PRISMA flow diagrams, and global maps. The guide emphasizes the rigorous reporting of 95% confidence intervals (CIs) and proper statistical methods to ensure scientific accuracy.

This skill guides the planning and auditing of analysis for linguistic manuscripts to ensure evidence supports theoretical claims. It covers quantitative modeling like mixed-effects models in R, phonetic measurement, and corpus statistics. It helps establish the analytic trail from raw data to generalization without fabricating results. It ensures proper statistical specification, effect size reporting, and handling of uncertainty, aligning with academic standards for credibility in language research.

A comprehensive skill designed to help researchers assess whether a manuscript is suitable for submission to the prestigious journal, Macroeconomics. It covers the journal's scope (macro-policy, growth, regional strategy), required methodological rigor (econometrics, model transparency), structural expectations, and common pitfalls, acting as a pre-submission positioning and rewriting framework.