Skill UI

A comprehensive guide for researchers submitting academic papers to ensure high standards of reproducibility and verifiable evidence. It covers critical areas such as data-availability statements, artifact management, provenance pinning (for LLMs and mining), and mapping claims directly to evidence, adhering to rigorous academic standards.

A comprehensive guide for authors submitting to top-tier security conferences (e.g., IEEE S&P). It details best practices for constructing the 'Related Work' section, focusing on defining a clear 'security delta' statement, mapping coverage across multiple attack vectors, managing resubmission overlaps (40% rule), and verifying citation attribution using tools like dblp and IEEE Xplore.

Provides comprehensive guidelines for achieving high reproducibility and verifiability in academic research submissions, particularly for software analysis, fuzzing, and bug finding. It details the necessity of pinning substrates (like benchmark versions and program SHAs), fixing stochastic parameters (seeds and budgets), and mapping every empirical claim to verifiable evidence to meet stringent academic evaluation criteria.

This guide assists researchers in transforming vague datasets or observations into robust, positive, and economics-driven research questions suitable for prestigious accounting journals (like JAE). It teaches key concepts, such as identifying economic friction, distinguishing between normative and positive questions, establishing plausible identification strategies, and mapping the topic to specific academic domains.

A comprehensive workflow guide mapping the entire academic submission lifecycle for JCAD&CG, from initial topic selection to final camera-ready stage. It organizes 12 key stages, identifying critical deliverables, decision points, and necessary steps at every phase to ensure a smooth and systematic publication process.

This guide teaches researchers how to accurately assign JEL (Journal of Economic Literature) classification codes to academic surveys. It details the three-level taxonomy structure, guiding users to select the primary field code, relevant method codes, and adjacent subject codes to ensure proper indexing and scope mapping within the American Economic Association's subject map.

This guide provides expert advice on structuring the literature review for top-tier finance journals (e.g., JFE). It teaches authors to move beyond simple chronological listing, instead mapping the core academic debates, pinpointing the exact boundaries of the closest existing work, and articulating concrete, theoretically motivated gaps that the new research fills. This is essential for convincing domain experts of novel contribution.

A comprehensive guide for establishing rigorous causal claims in law and economics. It details advanced econometric methodologies (DiD, RDD, IV, Event Study) required to defend the identification strategy—the mapping from a legal variation to the causal estimand—meeting the high standards of top academic journals.

This guide provides a structured framework for revising a manuscript after a 'revise-and-resubmit' (R&R), particularly for multi-paradigm fields like communication studies. It focuses on strategic academic writing: mapping conflicting reviewer demands (e.g., post-positivist vs. critical), managing the strict page budget (e.g., 35-page limit), and structuring the response memo to the editor-in-chief. It emphasizes framing the response as a principled trade-off rather than simply adding content.

This guide helps structure the conceptual framework (Theory of Change or Logic Model) for policy research. It moves beyond simple correlation by explicitly mapping the causal chain: identifying the policy lever, the underlying mechanism, and the predicted outcome. It is crucial for establishing external validity and defining scope conditions so that empirical findings can be generalized and actionable by policymakers in different settings.

A comprehensive guide for strengthening empirical causal claims for top-tier economics journals. This framework teaches how to move beyond simple OLS regressions by rigorously testing for causal identification (e.g., DID, IV, RDD) and, crucially, mapping the statistical estimate back to a meaningful economic parameter or mechanism. It emphasizes the dual requirement of credible identification and deep economic interpretability.

A comprehensive guide for authors to position their ML systems research by mapping the vast, scattered literature across multiple venues (OSDI, SOSP, NeurIPS, open-source projects). It details how to audit novelty, handle fast-moving advancements, and write a defensible delta statement against both academic and industrial prior work.