Discover how GRADEpro simplifies guideline development, enhances collaboration, and ensures transparency.
evidence.guide
Evidence intelligence platform: dashboards, docs, and tooling for trustworthy research quality.
A Claude skill for pre-submission peer review
Open-source skill to use Claude + Consensus for pre-submission mock peer review backed by citations to published, peer-reviewed literature.
AI in university assessment: do grades themselves have to go? - Analogue Resistance
Since I started teaching at the University of Amsterdam, roughly 20 years ago, assessment has become more and more schematic. Rules and regulations have prol…
A Cross‐Disciplinary Analysis of <span style="font-variant:small-caps;">AI</span> Policies in Academic Peer Review
ABSTRACT Rapid advances of artificial intelligence (AI) have substantially impacted the field of academic publishing. This study examines AI integration in peer review by analysing policies from 439 high‐ and 363 middle‐impact factor (IF) journals across disciplines. Using grounded theory, we identify patterns in AI policy adoption. Results show 83% of high‐IF journals have AI guidelines, with varying stringency across disciplines. Meanwhile, only 75% of middle‐IF journals have AI guidelines. Science, technology, and medicine (STM) disciplines exhibit stricter regulations, while humanities and social sciences adopt more lenient approaches. Key ethical concerns focus on confidentiality risks, accountability gaps, and AI's inability to replicate critical human judgement. Publisher policies emphasise transparency, human oversight, and restricted AI usage for auxiliary tasks only, such as grammar checks or reviewer finding. Disciplinary differences highlight the need for tailored guidelines that balance efficiency gains with research integrity. This study proposes collaborative frameworks for responsible AI integration. It focuses on accountability, transparency, and interdisciplinary policy development to address peer review challenges. , Key Points Application of AI tools in peer review has received increasing attention. Among the 439 high‐IF journals investigated, 83% provided AI policies for peer review, while 75% of middle‐IF journals did so. AI policies focus on confidentiality risks, accountability gaps, and capacity deficiencies of AI. AI policies for peer review vary according to the affiliated publishers and disciplines. Tailored AI guidelines need to be established according to the discipline.
No. 025: Navier-Stokes provenance dispute, Fields Medalists declaration, AI peer review - The Hallway Track
The Hallway Track No. 025 - How AI is affecting the practice of science
Reproducible research: methodological principles for transparent…
This Mooc proposes methodological principles for open and transparent science. It deals in a practical way with note-taking, computational documentation, replicability of analyses.
No. 013: Provenance vocabulary, Wikipedia contamination, OpenAI rare-disease diagnoses, ARA paper format - The Hallway Track
The Hallway Track No. 013 - How AI is affecting the practice of science
A template for creating open source contributor guidelines
Open source contributor guidelines should be easy to read, thorough, and friendly.
Colibri Social Documentation
Learn about Colibri's architecture, specification, and how to contribute.
Colibri Social Documentation
Learn about Colibri's architecture, specification, and how to contribute.
Social rules - Recurse Center
The RC social rules help create a friendly, intellectual environment where you can spend your energy on programming.
Blueprints Programs – Blueprints for Healthy Youth Development
Protective factors are conditions that buffer a person from exposure to risk by either reducing the impact of the risks or changing the way that one respond to risks.
Hypothesis | The #1 Social Annotation Tool for Higher Education
Hypothesis is the leading social annotation platform trusted by 300+ institutions to boost student engagement, comprehension, and critical thinking — seamlessly integrated into your LMS.
