Skill UI

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.

Provides a comprehensive pre-submission checklist for manuscripts targeting the Journal of Marketing (JM). It verifies adherence to strict, evolving academic guidelines, covering double-anonymization, specific formatting (e.g., 50-page limit, Times New Roman), exact statistic reporting, and mandatory inclusion of a Data Availability Statement. Essential for authors preparing for submission via ScholarOne.

Provides comprehensive guidelines for building high-quality, statistically rigorous tables and figures for Journal of Marketing (JM) submissions. It emphasizes adhering to AMA house style, ensuring results display exact p-values, standard errors, and interpretable effect sizes, while strictly managing the 50-page manuscript budget.

A comprehensive guide for researchers conducting advanced data analysis and reporting for top marketing journals. It details selecting the appropriate econometric estimator (e.g., ANOVA, DiD, IV, mediation models) based on the study design (experimental, observational, panel). It emphasizes meeting rigorous journal mandates, including exact p-values, standard errors, and effect sizes, and linking statistical findings to practical managerial claims.

This guide outlines strict guidelines for formatting tables and figures required for submission to the Journal of Marketing Research (JMR). It details managing the crucial 50-page manuscript limit versus the Web Appendix, ensuring all statistics adhere to AMA style (e.g., reporting exact p-values, standard errors, and effect sizes). It is designed for authors who need to transform raw results into publication-ready, self-contained, and compliant academic exhibits.

A comprehensive guide designed to help researchers target specific academic journals, using the Central University of Finance and Economics (CUFE) Journal as a primary example. It provides deep insights into the journal's scope, required structure, common acceptance criteria, and high-risk rejection areas for economics and management manuscripts. This skill helps users frame, revise, and optimize their papers for optimal publication fit.

Prepares datasets, code, glossed corpora, and sound files for a Language (LSA) manuscript. Covers reproducible quantitative pipelines, Leipzig-glossed fieldwork examples, phonetic scripts, community consent and anonymization ethics, and data-availability statements.

Prepares exhibits for linguistics manuscripts including numbered examples, interlinear glosses, IPA transcription, and quantitative tables. Ensures compliance with Language journal conventions like Leipzig Glossing Rules. Formats syntactic trees and figures for double-anonymous review. Does not generate data but structures existing linguistic evidence.

A comprehensive guide for revising technical submissions, particularly in computational linguistics and NLP. It details how to enforce linguistic scope discipline, present examples as verifiable arguments, structure the first page for maximum impact, anticipate reviewer limitations, and maintain strict terminology consistency for high-stakes academic publishing.

This tool enforces compliance with major international reporting guidelines (such as CONSORT, STROBE, and PRISMA) for clinical and observational studies. It guides authors through selecting the correct checklist, generating mandatory flow diagrams (e.g., participant flow), and ensuring rigorous reconciliation of numbers across the entire manuscript to meet the exacting standards of top-tier medical journals.

This skill guides authors on how to strategically position their machine learning research against complex academic landscapes. It covers comparing work to neighboring conferences (ICML, ICLR), analyzing contemporaneous preprints, and defining the exact technical delta from prior methods to minimize novelty risk for top-tier AI venues.

A comprehensive skill for assessing whether a neuroscience manuscript meets the exacting standards of a top-tier journal. It guides authors through evaluating the conceptual advance, mandatory multi-level integration (molecular to behavioral), the necessity of causal intervention, and adhering to strict structural and methodological requirements (e.g., STAR Methods, data availability). Ideal for researchers aiming to refine their work before submission.