量化个体科学影响的演变
Roberta Sinatra1,2, Dashun Wang3,4, Pierre Deville1,5
1Center for Complex Network Research and Physics Department, Northeastern University, Boston, MA 02115, USA.
概括
通过有影响力的出版物来衡量科学影响, 似乎随机分布在科学家的职业生涯中. 一个新的随机模型解释了这种模式,
科学领域:
- 图书识别技术
- 科学测量
- 职业发展轨迹分析
背景情况:
- 量化指标被广泛用于评估科学家的影响,但影响的出现和演变的基本动态仍然不太清楚.
- 现有的指标往往无法捕捉科学成功的复杂相互作用因素.
研究的目的:
- 在科学家的职业生涯中量化科学影响和生产力的变化.
- 通过分离生产力,个人能力和运气来解释科学成功的出现.
- 确定科学事业发展的普遍模式.
主要方法:
- 出版序列和相关影响指标的分析 (例如,引用,h指数).
- 基于"随机影响规则"的随机模型的开发.
- 通过预测个体科学家的影响演变和认可 (例如奖项) 来验证模型.
主要成果:
- 通过有影响力的出版物定义的科学影响,遵循科学家的出版物输出中的随机分布.
- 确定了一个稳定的个体参数"Q",描述每个科学家的影响潜力.
- 随机模型准确地预测了各种影响措施的时间演变,包括h指数和累计引用.
结论:
- 这项研究揭示了科学成功的基本随机影响规则.
- 一个新的随机模型为理解和预测科学事业轨迹提供了一个框架.
- 这项研究提供了关于生产力,能力和运气在获得科学认可方面的新见解.
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