hum-source-criticism

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Source Criticism

信息来源可信度评估

Overview

概述

Source criticism is a systematic method for evaluating whether information is trustworthy. Originally from historical methodology, it's now essential for navigating an information environment flooded with misinformation, opinion-as-fact, and AI-generated content.

信息来源可信度评估是一种系统判断信息是否可信的方法。它起源于历史研究方法论，如今在充斥着错误信息、以观点代事实以及AI生成内容的信息环境中，已成为必备技能。

Framework

框架

IRON LAW: No Source Is Automatically Trustworthy

Every source — including academic journals, government data, and news from
reputable outlets — has potential biases, errors, and limitations. Credibility
is assessed, not assumed. "It's from the New York Times / 中央社" is not
sufficient — WHAT are they reporting, based on WHAT evidence, and do other
sources corroborate it?

铁则：没有任何信息来源是天然可信的

所有信息来源——包括学术期刊、政府数据以及知名媒体的新闻——都可能存在偏见、错误和局限性。可信度需要被评估，而非默认。“这来自《纽约时报》/中央社”并不足够——他们报道的内容是什么？基于什么证据？其他来源是否能佐证？

Source Classification

来源分类

Primary sources: Direct evidence from the time/event (original documents, raw data, eyewitness accounts, original research, official records)

Secondary sources: Analysis or interpretation of primary sources (textbooks, review articles, news analysis, biographies)

Tertiary sources: Compilations of primary and secondary (encyclopedias, Wikipedia, databases) — starting points, not endpoints

原始来源：来自事件发生时期的直接证据（原始文档、原始数据、目击者陈述、原创研究、官方记录）

二手来源：对原始来源的分析或解读（教科书、综述文章、新闻分析、传记）

三级来源：原始来源与二手来源的汇编（百科全书、Wikipedia、数据库）——仅作为起点，而非终点

Four Tests of Source Credibility

信息来源可信度的四项验证方法

1. External Criticism — Is the source authentic?

Who created it? Are they who they claim to be?
When was it created? Is the date consistent?
Is it the original or has it been altered?
Is the publication/platform reputable?

2. Internal Criticism — Is the content reliable?

Does the author have expertise in this topic?
What is the author's potential bias or interest?
Is the evidence cited? Can it be verified?
Is the reasoning logical? Are conclusions supported by the evidence?

3. Triangulation — Do multiple independent sources agree?

Check 3+ independent sources (not copies of the same original report)
"Independent" means different authors, different organizations, different methods
Agreement across independent sources strengthens confidence

4. Currency — Is the information current enough?

When was it published? Has the situation changed since then?
For fast-moving topics (AI, policy, markets), even 6-month-old sources may be outdated

1. 外部验证——来源是否真实可信？

谁创建了该来源？他们是否如声称的那样？
创建时间是什么时候？日期是否一致？
这是原始版本还是被修改过的？
发布平台是否有信誉？

2. 内部验证——内容是否可靠？

作者是否在该主题上具备专业知识？
作者是否存在潜在的偏见或利益关联？
是否引用了证据？这些证据能否被验证？
推理是否符合逻辑？结论是否有证据支持？

3. 三角验证——多个独立来源是否达成一致？

核查3个及以上独立来源（并非同一原始报道的复制版本）
“独立”指不同作者、不同机构、不同研究方法
独立来源之间的一致性会提升可信度

4. 时效性——信息是否足够及时？

发布时间是什么时候？自发布以来情况是否发生变化？
对于快速变化的领域（AI、政策、市场），即使是6个月前的信息也可能已过时

Red Flags for Misinformation

错误信息的警示信号

Red Flag	Description
No author or organization identified	Who stands behind this claim?
Emotional language without evidence	Designed to provoke, not inform
No primary sources cited	Claims without traceable evidence
"Studies show" without naming the study	Vague appeals to authority
Single source amplified across many sites	Same claim copied, not independently verified
Too good to be true / too outrageous	Extreme claims require extreme evidence
URL/domain mimics reputable source	Fakecnn.com, bbc-news.co (not bbc.co.uk)

警示信号	描述
未标注作者或机构	谁为该主张背书？
使用情绪化语言却无证据支撑	旨在煽动情绪而非传递信息
未引用任何原始来源	主张无迹可寻的证据支持
仅称“研究表明”却未指明具体研究	模糊地诉诸权威
单一来源在多个平台被放大传播	同一主张被复制，未经过独立验证
好得离谱/过于耸人听闻	极端主张需要极端证据支撑
URL/域名模仿知名来源	如Fakecnn.com、bbc-news.co（而非bbc.co.uk）

Output Format

输出格式

markdown

undefined

markdown

undefined

Source Evaluation: {Source/Claim}

信息来源评估：{来源/主张}

Source Identity

来源信息

Author/Organization: {who}
Publication: {where}
Date: {when}
Type: Primary / Secondary / Tertiary

作者/机构：{主体}
发布平台：{平台}
发布日期：{时间}
类型：原始/二手/三级来源

Credibility Assessment

可信度评估

Test	Assessment	Evidence
External (authentic?)	✓/⚠/✗	{reasoning}
Internal (reliable?)	✓/⚠/✗	{reasoning}
Triangulation (corroborated?)	✓/⚠/✗	{other sources checked}
Currency (current?)	✓/⚠/✗	{relevance of date}

验证方法	评估结果	依据
外部验证（是否真实？）	✓/⚠/✗	{推理过程}
内部验证（是否可靠？）	✓/⚠/✗	{推理过程}
三角验证（是否有佐证？）	✓/⚠/✗	{核查的其他来源}
时效性（是否及时？）	✓/⚠/✗	{日期的相关性}

Red Flags

警示信号

{any detected red flags}

{检测到的警示信号}

Verdict

结论

Credibility: High / Moderate / Low
Recommended action: {trust / verify further / discard}

undefined

可信度：高/中等/低
建议行动：信任/进一步验证/弃用

undefined

Examples

示例

Correct Application

正确应用示例

Scenario: Evaluating a viral social media post claiming "Taiwan's GDP will surpass South Korea's by 2027"

Test	Assessment	Evidence
External	⚠	Anonymous account, no institutional affiliation, chart has no data source
Internal	✗	Uses nominal GDP (not PPP), cherry-picks semiconductor sector projection, ignores exchange rate volatility
Triangulation	✗	IMF and World Bank projections show no such convergence; no reputable analyst makes this claim
Currency	✓	Posted this month

Red flags: Emotional headline ("Taiwan DESTROYS Korea"), no primary data source cited, single unsourced chart Verdict: Low credibility — discard ✓

场景：评估一则在社交媒体上传播的声称“中国台湾地区GDP将在2027年超过韩国”的帖子

验证方法	评估结果	依据
外部验证	⚠	匿名账号，无机构关联，图表未标注数据来源
内部验证	✗	使用名义GDP（而非购买力平价PPP），刻意挑选半导体行业的预测数据，忽略汇率波动影响
三角验证	✗	IMF和世界银行的预测未显示该趋同趋势，没有知名分析师提出此主张
时效性	✓	本月发布

警示信号：情绪化标题（“中国台湾地区‘碾压’韩国”），未引用任何原始数据来源，仅含一张无来源图表结论：可信度低——建议弃用 ✓

Incorrect Application

错误应用示例

"This is from Reuters, so it must be true" → Credibility assumed, not assessed. Even reputable sources can be wrong, outdated, or framing an issue in a particular way. Violates Iron Law.

“这来自路透社，所以肯定是真的” → 直接默认可信度，未进行评估。即使是知名来源也可能出错、信息过时或带有特定立场的框架叙事。违反了铁则。

Gotchas

注意事项

Bias ≠ unreliable: Every source has a perspective. A labor union's report on working conditions is biased but may contain accurate data. Assess bias AND accuracy separately.
Wikipedia is a starting point: It's a tertiary source with references. Follow the references to primary/secondary sources. Don't cite Wikipedia as evidence — cite what Wikipedia cites.
AI-generated content: AI can produce convincing but fabricated "sources" (fake papers, fake quotes, fake statistics). Verify that cited sources actually exist.
Consensus ≠ truth, but it's a strong signal: Scientific consensus (climate change, vaccine safety) is the strongest available evidence. Lone dissenting "experts" who contradict consensus need extraordinary evidence.
Source credibility is domain-specific: A cardiologist is a credible source on heart disease but not on economics. Match expertise to the claim.

偏见≠不可靠：所有来源都有其立场。工会发布的工作环境报告可能带有偏见，但也可能包含准确数据。需分别评估偏见与准确性。
Wikipedia仅作为起点：它是带有参考文献的三级来源。需追踪参考文献至原始/二手来源。不要直接引用Wikipedia作为证据——引用Wikipedia所引用的来源。
AI生成内容：AI可能生成看似可信但实为伪造的“来源”（假论文、假引用、假统计数据）。需验证所引用的来源真实存在。
共识≠真理，但却是强烈信号：科学共识（如气候变化、疫苗安全性）是目前最有力的证据。与共识相悖的个别持异议“专家”需要提供非同寻常的证据。
来源可信度具有领域特异性：心脏病学家在心脏病领域是可信来源，但在经济学领域则不然。需确保专家的专业领域与所主张的内容匹配。

References

参考文献

For CRAAP test (Currency, Relevance, Authority, Accuracy, Purpose), see
```
references/craap-test.md
```
For fact-checking tools and databases, see
```
references/fact-check-tools.md
```

关于CRAAP测试（时效性、相关性、权威性、准确性、目的性），请参阅
```
references/craap-test.md
```
关于事实核查工具与数据库，请参阅
```
references/fact-check-tools.md
```