Beyond the Binary: The System of All-round Evaluation of Research and Its Practices in China

• URL: http://arxiv.org/abs/2509.08546v1
• Date: Wed, 10 Sep 2025 12:52:08 GMT
• Title: Beyond the Binary: The System of All-round Evaluation of Research and Its Practices in China
• Authors: Yu Zhu, Jiyuan Ye,
• Abstract summary: This paper introduces the System of All-round Evaluation of Research (SAER), a framework that integrates form, content, and utility evaluations with six key elements.<n>The comprehensive system proposes a trinity of three evaluation dimensions, combined with six evaluation elements, which would help academic evaluators and researchers reconcile binary oppositions in evaluation methods.
• Score: 3.6998581528902625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The lack of a macro-level, systematic evaluation theory to guide the implementation of evaluation practices has become a key bottleneck in the reform of global research evaluation systems. By reviewing the historical development of research evaluation, this paper highlights the current binary opposition between qualitative and quantitative methods in evaluation practices. This paper introduces the System of All-round Evaluation of Research (SAER), a framework that integrates form, content, and utility evaluations with six key elements. SAER offers a theoretical breakthrough by transcending the binary, providing a comprehensive foundation for global evaluation reforms. The comprehensive system proposes a trinity of three evaluation dimensions, combined with six evaluation elements, which would help academic evaluators and researchers reconcile binary oppositions in evaluation methods. The system highlights the dialectical wisdom and experience embedded in Chinese research evaluation theory, offering valuable insights and references for the reform and advancement of global research evaluation systems.

Related papers

• InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem [87.30601926271864]
  InnoEval is a deep innovation evaluation framework designed to emulate human-level idea assessment.<n>We apply a heterogeneous deep knowledge search engine that retrieves and grounds dynamic evidence from diverse online sources.<n>We construct comprehensive datasets derived from authoritative peer-reviewed submissions to benchmark InnoEval.
  arXiv  Detail & Related papers  (2026-02-16T00:40:31Z)
• The Story is Not the Science: Execution-Grounded Evaluation of Mechanistic Interpretability Research [56.80927148740585]
  We address the challenges of scalability and rigor by flipping the dynamic and developing AI agents as research evaluators.<n>We use mechanistic interpretability research as a testbed, build standardized research output, and develop MechEvalAgent.<n>Our work demonstrates the potential of AI agents to transform research evaluation and pave the way for rigorous scientific practices.
  arXiv  Detail & Related papers  (2026-02-05T19:00:02Z)
• Preliminary suggestions for rigorous GPAI model evaluations [0.0]
  This document presents a preliminary compilation of general-purpose AI (GPAI) evaluation practices.<n>It includes suggestions for human uplift studies and benchmark evaluations.<n>Suggestions are organised across four stages in the evaluation life cycle: design, implementation, execution and documentation.
  arXiv  Detail & Related papers  (2025-07-22T03:27:42Z)
• SPHERE: An Evaluation Card for Human-AI Systems [75.0887588648484]
  We present an evaluation card SPHERE, which encompasses five key dimensions.<n>We conduct a review of 39 human-AI systems using SPHERE, outlining current evaluation practices and areas for improvement.
  arXiv  Detail & Related papers  (2025-03-24T20:17:20Z)
• Good Idea or Not, Representation of LLM Could Tell [86.36317971482755]
  We focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. We release a benchmark dataset from nearly four thousand manuscript papers with full texts, meticulously designed to train and evaluate the performance of different approaches to this task. Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs.
  arXiv  Detail & Related papers  (2024-09-07T02:07:22Z)
• Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition [70.60872754129832]
  First NeurIPS competition on unlearning sought to stimulate the development of novel algorithms. Nearly 1,200 teams from across the world participated. We analyze top solutions and delve into discussions on benchmarking unlearning.
  arXiv  Detail & Related papers  (2024-06-13T12:58:00Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [51.26815896167173]
  We present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions.<n>Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices.<n>Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• Hierarchical Evaluation Framework: Best Practices for Human Evaluation [17.91641890651225]
  The absence of widely accepted human evaluation metrics in NLP hampers fair comparisons among different systems and the establishment of universal assessment standards. We develop our own hierarchical evaluation framework to provide a more comprehensive representation of the NLP system's performance. In future work, we will investigate the potential time-saving benefits of our proposed framework for evaluators assessing NLP systems.
  arXiv  Detail & Related papers  (2023-10-03T09:46:02Z)
• Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation [136.16507050034755]
  Existing human evaluation studies for summarization either exhibit a low inter-annotator agreement or have insufficient scale. We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which is based on fine-grained semantic units. We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of 22,000 summary-level annotations over 28 top-performing systems.
  arXiv  Detail & Related papers  (2022-12-15T17:26:05Z)
• DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation [24.12886646161467]
  We conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation.
  arXiv  Detail & Related papers  (2022-06-22T05:17:50Z)
• How to Evaluate Your Dialogue Models: A Review of Approaches [2.7834038784275403]
  We are first to divide the evaluation methods into three classes, i.e., automatic evaluation, human-involved evaluation and user simulator based evaluation. The existence of benchmarks, suitable for the evaluation of dialogue techniques are also discussed in detail.
  arXiv  Detail & Related papers  (2021-08-03T08:52:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.