新的纸张级分类对图书统计学的影响
Marcos Peña-Rocha1, Rocío Gómez-Crisóstomo1, Vicente P Guerrero-Bote1
1Departamento de Información y Comunicación, Universidad de Extremadura, Badajoz, Spain.
Frontiers in research metrics and analytics
|March 21, 2025
概括
本研究比较了两个科学文档分类系统. 一个纸一纸的方法减少了类别分配,并提供了比全科学期刊分类 (ASJC) 更统一的文献计量指标分布.
科学领域:
- 图书统计学 图书统计学
- 科学文件分类科学文件分类
- 信息科学 信息科学 信息科学
背景情况:
- 传统的基于期刊的分类系统,如Scopus的全科学期刊分类 (ASJC),将文档分配到预定义的类别中.
- 这些系统可能不能准确地反映现代研究的跨学科性质或引用的特定背景.
研究的目的:
- 为了比较分析基于Scopus期刊的分类系统 (分数模型) 与一个新的项目逐项分类系统.
- 评估引用来源对文档分类和文献计量指标表现的影响.
主要方法:
- 这项研究将基于Scopus期刊的分配方法适应为分数模型.
- 开发了第二个系统,使用基于引用来源的重新分类引用的项目逐项分类.
- 在Scopus领域和类别层面进行了比较,并进行了文献计量指标分析.
主要成果:
- 纸质系统减少了每个文档分配的类别的数量,比ASJC产生了更高的单一类别分配.
- 使用纸张级系统的重新分类突出了类型大小的差异,扩大了最大的类型,缩小了最小的类型.
- 项目逐项系统在正常化影响方面产生了更均的分布,并更统一地调整了卓越值.
结论:
- 纸张分类系统为科学文档分类提供了更细致和潜在的准确方法.
- 这种方法可以更精细地分析文献计量指标,特别是规范化影响和卓越指标.
- 这些发现表明,项目级分类有助于理解研究影响和跨学科联系的优势.
相关概念视频
00:59How Data are Classified: Numerical Data
27.6K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
27.6K
01:31Classification of Systems-II
132
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
132
01:26Classification of Systems-I
164
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
164
01:30Classification of Signals
365
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
365
01:11How Data are Classified: Categorical Data
31.5K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
31.5K
01:29Aggregates Classification
295
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
295