Work

Annotation Codebook A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports

Year: 2024

Type: report

Abstract: The codebook is an output of the study “A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports”, which combines qualitative human annotation and transformer-based machine-learning approaches to assess characteristics of grant proposal peer review reports submitted to the Swiss National Science Foundation (SNSF). The codebook includes detailed instructions and examples for the human annotators on how to label the sentences of peer review reports according to 12 categories reflecting the characteristics of grant peer review reports of interest to research funding agencies.

DOI: 10.46446/codebook-peer-review-assessment-ml

Authors: Michaela Strinzel, Gabriel Okasa, Anne Jorstad, Stefan Müller, Alberto de Léon, Matthias Egger, Katrin Milzow

Institutions: Swiss National Science Foundation, University College Dublin, University of Bern

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Funder: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

OpenAlex ID: W4404607904

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