## 4 Discussion
### Comparison with Related Work  
In this section we dissect earlier reports on quantitative analysis of scientific texts, underlying similarities and differences. The language complexity of scientific texts is calculated in [35]. Language complexity is divided in syntactic complexity and lexical complexity. The formulas used to measure these two types of complexity are based on the statistical properties of the texts, such as the number of words, number of sentences, number of words per sentence, length of nouns, verbs, clauses, to name but a few indicators. In the report, the authors compare scientific texts written in English by what they presumed are native speakers versus those written by what they also pre-supposed are non-native speakers. Interestingly, though, is that differences in lexical complexity are marginal when both sets of scientific articles are compared. We think that this similarity complements our quantitative results regarding the narrow inter-quartile range we present for the Hurst parameter obtained for scientific articles. Experiments using machine learning to identify the discourse structure in scientific texts are given in [36]. The authors pre-defined \(11\) Core Scientific Concepts: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion Then, machine learning classifiers (support vector machines and conditional random fields) were trained on a corpus of 265 full articles in biochemistry and chemistry to recognize such Core Scientific Concepts within texts. One of the steps to train classifiers consisted in dividing the documents into 10 unequal segments. To train the classifiers, they invented 16 different variables to categorize scientific texts. We think their segmentation is arbitrary. By using the Hurst parameter, it is the algorithm that discovers how many sub-series shall be used and how to normalized them. Also, with our approach the Hurst parameter is the only variable needed to discover whether there are linguistic patterns is the texts. Clustering was applied to analyze scientific texts and newspaper reports [7]. They studied 45 newspaper articles and 36 scientific articles by using three clustering algorithms: Expectation Maximization (EM), SKM and sIB. In both cases the sIB algorithm was the best performer. Their study was aimed, however, to identify what clustering algorithm could be used in a text classification task, rather than drawing conclusions about the characteristic of the texts themselves. A quantitative study of scientific articles that rests on the Hallidayan notion of _theme_ is presented in [37]. The authors claim that understanding of what scientists select as themes offer a vision at how scientific texts are structured in terms of what writers select as the points of departure of their message. They also think that tracing such starting points through the text reveals its structure, since it is from those starting points that the message in the text is developed. The authors employ a quantitative measure known as _thematic density index_. They analyzed 30 articles from the scientific journal called _Cell_. Their data suggest that articles begin with a simple thematic development in the introduction section before settling into an _anchored development_ in the rest of the text. Even though the corpus they employ is roughly a tenth of our corpus, and that the discipline of their corpus is biology, they found that writers tend to retain one or a small number of topical themes beyond the introduction section. We concur with such conclusions. However, they felt obliged to analyze each section of the articles and we only used one metric to conclude that scientific texts are self-similar. In another study ([38]) 500 abstracts of scientific research articles published in 50 journals across five science disciplines (Earth, Formal, Life, Physical and Social Sciences) were analyzed by quantifying how many abstracts adhere to the _Introduction_, _Purpose_, _Method_, _Results and Conclusion_ structure.