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Scaling social science research
OpenAI 的一项核心工作是帮助科学家加快进度、攻克更难的问题。今天， OpenAI 的 Economic Research Team 发布了开源工具包 GABRIEL：它利用 GPT 将非结构化的文本和图像转化为可量化的测度，旨在帮助经济学家、社会科学家和数据科学家对大规模的定性数据进行研究。
定性数据往往能讲出最丰富的故事——人们说什么、写什么、教什么、争论什么、经历什么。它覆盖课堂大纲、访谈、社交媒体、照片等各类内容，数量巨大。但把这类资料变成严谨的证据极为耗时，许多情况下根本难以实现。太多社会科学研究因此不得不放弃重要方向，原因不是数据不存在，而是无法分析。
GABRIEL 的目标是让定性数据更易被利用。研究者可以用日常语言描述想要测量的东西——比如“这则招聘信息对家庭友好吗？”——然后把同一问题一致地应用到成千上万（甚至上百万）篇文档上，为每条返回一个评分。这样研究者就能把重复标注的时间省下来，把精力放在真正需要专业判断的环节：决定测量什么、验证结果和谨慎得出结论。
举例来说， GABRIEL 可以分析大量学术论文，识别所用方法并观察其随时间的演进；可以审视课程大纲，衡量不同科目或技能被关注的程度；可以为欧洲每个小镇提取结构化的历史细节；也可以扫描海量顾客评论，发现人们最看重的模式。在我们的一篇论文中，我们对 GPT 在各种定性数据标注场景下进行了基准测试，结果显示其准确性很高。
除了测量功能， GABRIEL 还提供研究人员常用的实用工具，包括在列不匹配时合并数据集、智能去重、段落编码、生成新的科学假说，以及从文本中去识别化个人信息以保护隐私。
GABRIEL 已作为开源的 Python 库 发布，并提供入门教程笔记本（在 Colab 上）供使用。它的设计门槛低、技术要求不高。我们会根据学术界的反馈持续改进 GABRIEL，希望这项工具能帮助更多研究者将定性数据和人的故事带入他们的研究之中。
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A core part of our work at OpenAI is enabling scientists to move faster and solve harder problems. Today, our Economic Research Team is releasing GABRIEL: an open-source toolkit that uses GPT to turn unstructured text and images into quantitative measurements. It is designed for economists, social scientists, and data scientists to study qualitative data at scale.
Qualitative data tells the richest stories about the world – what people say, write, teach, argue, and experience. It spans everything from syllabi and interviews to social media and photographs. There is a tremendous amount of it. But transforming that type of data into rigorous evidence is incredibly time-consuming. Often it isn't feasible at all. In too many cases, social scientists are forced to forego important avenues of research, not because the data doesn’t exist, but because it’s impossible to analyze.
GABRIEL is built to make qualitative data much more accessible. It allows researchers to describe what they want to measure in everyday words—like “how family-friendly is this job listing?”—and then applies that same question consistently across thousands (or millions) of documents, returning a score for each one. This lets researchers spend less time on repetitive data labeling and more time on the work that actually requires expertise: choosing what to measure, validating results, and drawing careful conclusions.
For example, GABRIEL can analyze a large collection of scientific papers to see what specific methods are used and how they evolve over time. It can look at course curricula to measure how much attention is given to different subjects or skills. It can extract structured historical details for every small town across Europe, or examine a trove of customer reviews and discover patterns in what people value most. In our paper, we benchmark GPT at labeling qualitative data across many use cases and find that it is highly accurate.
Beyond this type of measurement, GABRIEL also provides practical tools researchers often need. These include merging datasets even when the columns don’t match, smart deduplication, passage coding, ideating new scientific theories, and deidentifying personal information from text to preserve privacy.
GABRIEL is available now as an open-source Python library, with a tutorial notebook to get started. It is designed to require minimal technical background. We’ll keep improving GABRIEL over time based on feedback from the academic community. We hope this tool will help more researchers bring the richness of qualitative data and human stories into their work.
via OpenAI News
OpenAI 的一项核心工作是帮助科学家加快进度、攻克更难的问题。今天， OpenAI 的 Economic Research Team 发布了开源工具包 GABRIEL：它利用 GPT 将非结构化的文本和图像转化为可量化的测度，旨在帮助经济学家、社会科学家和数据科学家对大规模的定性数据进行研究。
定性数据往往能讲出最丰富的故事——人们说什么、写什么、教什么、争论什么、经历什么。它覆盖课堂大纲、访谈、社交媒体、照片等各类内容，数量巨大。但把这类资料变成严谨的证据极为耗时，许多情况下根本难以实现。太多社会科学研究因此不得不放弃重要方向，原因不是数据不存在，而是无法分析。
GABRIEL 的目标是让定性数据更易被利用。研究者可以用日常语言描述想要测量的东西——比如“这则招聘信息对家庭友好吗？”——然后把同一问题一致地应用到成千上万（甚至上百万）篇文档上，为每条返回一个评分。这样研究者就能把重复标注的时间省下来，把精力放在真正需要专业判断的环节：决定测量什么、验证结果和谨慎得出结论。
举例来说， GABRIEL 可以分析大量学术论文，识别所用方法并观察其随时间的演进；可以审视课程大纲，衡量不同科目或技能被关注的程度；可以为欧洲每个小镇提取结构化的历史细节；也可以扫描海量顾客评论，发现人们最看重的模式。在我们的一篇论文中，我们对 GPT 在各种定性数据标注场景下进行了基准测试，结果显示其准确性很高。
除了测量功能， GABRIEL 还提供研究人员常用的实用工具，包括在列不匹配时合并数据集、智能去重、段落编码、生成新的科学假说，以及从文本中去识别化个人信息以保护隐私。
GABRIEL 已作为开源的 Python 库 发布，并提供入门教程笔记本（在 Colab 上）供使用。它的设计门槛低、技术要求不高。我们会根据学术界的反馈持续改进 GABRIEL，希望这项工具能帮助更多研究者将定性数据和人的故事带入他们的研究之中。
----------------------
A core part of our work at OpenAI is enabling scientists to move faster and solve harder problems. Today, our Economic Research Team is releasing GABRIEL: an open-source toolkit that uses GPT to turn unstructured text and images into quantitative measurements. It is designed for economists, social scientists, and data scientists to study qualitative data at scale.
Qualitative data tells the richest stories about the world – what people say, write, teach, argue, and experience. It spans everything from syllabi and interviews to social media and photographs. There is a tremendous amount of it. But transforming that type of data into rigorous evidence is incredibly time-consuming. Often it isn't feasible at all. In too many cases, social scientists are forced to forego important avenues of research, not because the data doesn’t exist, but because it’s impossible to analyze.
GABRIEL is built to make qualitative data much more accessible. It allows researchers to describe what they want to measure in everyday words—like “how family-friendly is this job listing?”—and then applies that same question consistently across thousands (or millions) of documents, returning a score for each one. This lets researchers spend less time on repetitive data labeling and more time on the work that actually requires expertise: choosing what to measure, validating results, and drawing careful conclusions.
For example, GABRIEL can analyze a large collection of scientific papers to see what specific methods are used and how they evolve over time. It can look at course curricula to measure how much attention is given to different subjects or skills. It can extract structured historical details for every small town across Europe, or examine a trove of customer reviews and discover patterns in what people value most. In our paper, we benchmark GPT at labeling qualitative data across many use cases and find that it is highly accurate.
Beyond this type of measurement, GABRIEL also provides practical tools researchers often need. These include merging datasets even when the columns don’t match, smart deduplication, passage coding, ideating new scientific theories, and deidentifying personal information from text to preserve privacy.
GABRIEL is available now as an open-source Python library, with a tutorial notebook to get started. It is designed to require minimal technical background. We’ll keep improving GABRIEL over time based on feedback from the academic community. We hope this tool will help more researchers bring the richness of qualitative data and human stories into their work.
via OpenAI News
MCP not working in Research in claude.ai
Feb 13, 17:56 UTC
Investigating - We are currently investigating this issue.
via Claude Status - Incident History
Feb 13, 17:56 UTC
Investigating - We are currently investigating this issue.
via Claude Status - Incident History
Elevated errors on Opus 4.6 Fast Mode
Feb 13, 17:13 UTC
Investigating - We are currently investigating this issue.
via Claude Status - Incident History
Feb 13, 17:13 UTC
Investigating - We are currently investigating this issue.
via Claude Status - Incident History
