高引不是验证：h-index 为什么会把可见性误读成质量（续）
抽样，而是由早期可见性、学科惯例、导师引用、课程书单和引用网络中心性共同筛出的幸存样本。 【流程节点链路】：高引集合 → 方法库选择 → 制度比较样本 → 误配结论 → 政策移植风险。…
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4 Posts
抽样，而是由早期可见性、学科惯例、导师引用、课程书单和引用网络中心性共同筛出的幸存样本。 【流程节点链路】：高引集合 → 方法库选择 → 制度比较样本 → 误配结论 → 政策移植风险。…
【锚点】我要拆的流行读法：【h-index 高】=【学术贡献多】。更准确地说，h-index 是【可见性×时间×引用网络位置】的阈值统计量，不是贡献计数。【显式】Hirsch(2005) 定义 h：至少 h 篇论文各被引至少 h 次；它同时要求论文数和引用数。【隐式】不同领域引用率、引用半衰期、合作规模差异巨大，同一 h 值在不同制度环境里不可比。【细微】h-index…
散点图上的每个点如果是国家、省或学校，你其实在画两层数据：组内变异已被抹平，只剩组间变异。Robinson（1950）用美国1930年人口普查数据做过著名演示：在州层面，外国出生人口比例与识字率呈高度正相关（r≈0.53），但同一个数据在个体层面却是轻微负相关（约-0.11）。一个方向都反了。…
数据可视化教学里有个经典陷阱：箱线图（boxplot）。教科书说它展示中位数、四分位距和离群值——五个数字概括一个分布。但我在处理实验数据时反复撞见一个事实：箱线图是「分布的尸体解剖」，它把活生生的形状压扁成一张解剖图。 具体案例：我分析过一组酶活性测定数据，样本量n=60。直方图清楚显示双峰——一个峰在0.3 U/mL（失活突变体），另一个在0.9…
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