Standards of Reporting

Our Commitment to Transparent and Rigorous Reporting

At Global Journals®, we believe that clear, complete, and reproducible reporting is fundamental to trustworthy science. Our reporting standards ensure that readers, reviewers, and future researchers can understand exactly what was done, why, and how. In every article we publish, we expect authors to meet high benchmarks for transparency, integrity, and methodological clarity.

An academic team passionately discusses research findings during a professional conference on education and science.

Why Reporting Standards Matter

They enable verification, replication, and reuse of research results.

They strengthen trust in published work by making methodology, data, and analysis fully transparent.

They help reduce ambiguity, bias, and selective reporting.

They support meta-analyses and systematic reviews by ensuring essential information is consistently provided.

Core Principles of Our Reporting Policy

Completeness

Authors must provide all essential details, from study rationale to data processing, so that readers can understand and evaluate the work.

Methodological Transparency

Information on study design, sampling, measurement methods, statistical analyses, and protocols should be clearly stated and justified.

Consistency & Structure

Manuscripts should follow a logical and conventional structure (e.g. Introduction, Methods, Results, Discussion) to promote readability and comparability.

Handling AI and Automated Tools

Use of AI tools (e.g., for text generation, data analysis) must be disclosed, explained, and justified. Authors must clarify how AI was used, what safeguards were in place, and confirm that human oversight guided key decisions.

Recommended Reporting Guidelines

Depending on your study type, we ask that you consult and adhere to relevant reporting frameworks, such as:

  • CONSORT – Randomized controlled trials
  • PRISMA – Systematic reviews & meta-analyses
  • STROBE – Observational studies
  • CARE – Clinical case reports
  • ARRIVE – Animal research
  • STARD, TRIPOD, SPIRIT, SRQR / COREQ, among others

Examples & Best Practices

  • If using AI or machine learning methods, describe data splits, hyperparameter tuning, validation, reproducibility protocols, error analysis, and limitations. (Emerging standards such as REFORMS offer guidance in this domain.)
  • For randomized trials, specify randomization procedure, allocation concealment, blinding, primary and secondary outcomes, intention-to-treat versus per-protocol analyses.
  • For observational studies, clarify inclusion criteria, variable definitions, handling of missing data, potential confounders, and sensitivity analyses.
  • In all cases, authors should be transparent about any deviations from originally planned methods and how those affect interpretation.

Implementation in Our Review Process

  • On submission, manuscripts may be assessed for compliance with relevant reporting checklists.
  • Editors or editorial staff may request authors to revise or complete missing elements before peer review.
  • Reviewers may use reporting guidelines as a tool to assess completeness and clarity.
  • Failure to meet core reporting standards may lead to revision requests or rejection.