Pub Date : 2020-09-07DOI: 10.1515/9783110684384-fm
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Pub Date : 2020-09-07DOI: 10.1515/9783110684384-009
D. Bachmann
“Stemmatology usually works with texts that change during their copying history.” If we conduct a small experiment of metaphorically zooming out and replacing the nouns in this sentence with nouns from a higher, more general category, we could say: “Genealogical science usually works with sequences that change during their transmission.” Some sciences for which this statement is applicable – though not all of them – will be the focus of this chapter. The formulation “sequences that change during transmission” hints at e v o l u t i o n a r y theory, although the concept of evolution more specifically entails mutation and selection as agents of change, and therefore carries strong biological connotations. Nonetheless, it has been used to convey different notions of processes of change which lead to hierarchical or temporally successive structures in various disciplines; thus, we can speak of biological evolution, text evolution, language evolution, the evolution of writing materials, and so on. The main visual metaphor for such structures, and the only figure in Darwin’s On the Origin of Species (1859), is the t r e e. The tree as a mathematical, analytical structure has been used, in turn, for a huge number of purposes, be it in one of its first attested usages, as a family tree for aristocratic families (see Lima 2014, 29); as a stemma codicum; or as a way of displaying folder and file structures on a computer. As Lima (2011, 43) points out, the tree has been appreciated on the one hand and attacked on the other (and not only in stemmatology). Yet it has survived criticism and continues to be widely used. So far, in this book we have looked at many kinds of stemmatic trees. In this chapter, we will focus on fellow trees from other disciplines, which together form the forest of “trees of history”, as O’Hara (1996) proposed to call some of them. The application of the tree model in science as an analytical tool is – as already stated – very broad and has had a special role as a “tool of thought” in Europe (KlapischZuber 2007, 293). The habitat of our forest is indeed vast. In fact, it is so large that we will not be able to cover all the applications of trees (for which Lima 2014, among others, could be consulted); instead, we limit ourselves to some of the disciplines most intimately related to stemmatology: linguistics, cultural evolution, musicology, and biology. What are the parallels, what are the differences, what can we learn from each other, what can we borrow or incorporate, and what are the interfaces stemmatology shares with these sciences? These are some of the leading questions to keep in mind when reading this chapter. Phylogenetics (8.1) has functioned as a donor of many computational tools (see 5.2, 5.4) to stemmatology. Linguistics (8.2) makes complex genealogical judgements just as stemmatology does, albeit with a focus on language as a whole, not on a single work. Anthropological phylomemetics (8.3; an umbrella term proposed by
“词干学通常研究在复制过程中发生变化的文本。”如果我们做一个小实验，把这句话中的名词缩小，用更高、更一般的类别的名词代替，我们可以说:“家谱科学通常研究的是在传递过程中发生变化的序列。”这句话适用的一些科学——尽管不是全部——将是本章的重点。“在传播过程中发生变化的序列”这一提法暗示了人类在进化理论中的地位，尽管进化的概念更具体地将突变和选择作为变化的媒介，因此具有强烈的生物学内涵。尽管如此，它已被用来传达变化过程的不同概念，这些变化过程导致不同学科的等级或时间连续结构;因此，我们可以说生物进化、文本进化、语言进化、书写材料的进化等等。这种结构的主要视觉隐喻，也是达尔文的《物种起源》(1859)中唯一的人物，是树。树作为一种数学的、分析的结构，反过来又被用于大量的目的，无论是在它的第一个被证实的用法之一，作为贵族家庭的家谱(见Lima 2014, 29);作为茎子叶;或者作为在计算机上显示文件夹和文件结构的一种方式。正如Lima(2011, 43)指出的那样，这棵树一方面受到赞赏，另一方面受到攻击(不仅在系统学上)。然而，它经受住了批评，并继续被广泛使用。到目前为止，在这本书中，我们已经研究了许多种类的有茎树。在本章中，我们将重点关注来自其他学科的同类树，它们共同构成了“历史之树”的森林，奥哈拉(O’hara, 1996)提议将其中一些树称为“历史之树”。如前所述，树形模型作为一种分析工具在科学中的应用非常广泛，并且在欧洲作为一种“思想工具”发挥了特殊的作用(KlapischZuber 2007, 293)。我们森林的栖息地确实很大。事实上，它是如此之大，以至于我们无法涵盖树木的所有应用(可以咨询利马2014等);相反，我们把自己限制在与系统学最密切相关的一些学科上:语言学、文化进化、音乐学和生物学。它们有什么相似之处，有什么不同之处，我们可以从彼此身上学到什么，我们可以借鉴或结合什么，系统学与这些科学有什么共同之处?这些是在阅读本章时要牢记的一些主要问题。系统遗传学(8.1)为系统学提供了许多计算工具(见5.2,5.4)。语言学(8.2)就像词源学一样，做出复杂的谱系判断，尽管它关注的是整个语言，而不是某一部作品。人类学系谱学(8.3;(C. J. Howe和Windram 2011年提出的总称)一方面与文化文物树(例如与法典学、书籍装订类型相关的材料)有关，另一方面与文化文物树有关
{"title":"8 Evolutionary models in other disciplines","authors":"D. Bachmann","doi":"10.1515/9783110684384-009","DOIUrl":"https://doi.org/10.1515/9783110684384-009","url":null,"abstract":"“Stemmatology usually works with texts that change during their copying history.” If we conduct a small experiment of metaphorically zooming out and replacing the nouns in this sentence with nouns from a higher, more general category, we could say: “Genealogical science usually works with sequences that change during their transmission.” Some sciences for which this statement is applicable – though not all of them – will be the focus of this chapter. The formulation “sequences that change during transmission” hints at e v o l u t i o n a r y theory, although the concept of evolution more specifically entails mutation and selection as agents of change, and therefore carries strong biological connotations. Nonetheless, it has been used to convey different notions of processes of change which lead to hierarchical or temporally successive structures in various disciplines; thus, we can speak of biological evolution, text evolution, language evolution, the evolution of writing materials, and so on. The main visual metaphor for such structures, and the only figure in Darwin’s On the Origin of Species (1859), is the t r e e. The tree as a mathematical, analytical structure has been used, in turn, for a huge number of purposes, be it in one of its first attested usages, as a family tree for aristocratic families (see Lima 2014, 29); as a stemma codicum; or as a way of displaying folder and file structures on a computer. As Lima (2011, 43) points out, the tree has been appreciated on the one hand and attacked on the other (and not only in stemmatology). Yet it has survived criticism and continues to be widely used. So far, in this book we have looked at many kinds of stemmatic trees. In this chapter, we will focus on fellow trees from other disciplines, which together form the forest of “trees of history”, as O’Hara (1996) proposed to call some of them. The application of the tree model in science as an analytical tool is – as already stated – very broad and has had a special role as a “tool of thought” in Europe (KlapischZuber 2007, 293). The habitat of our forest is indeed vast. In fact, it is so large that we will not be able to cover all the applications of trees (for which Lima 2014, among others, could be consulted); instead, we limit ourselves to some of the disciplines most intimately related to stemmatology: linguistics, cultural evolution, musicology, and biology. What are the parallels, what are the differences, what can we learn from each other, what can we borrow or incorporate, and what are the interfaces stemmatology shares with these sciences? These are some of the leading questions to keep in mind when reading this chapter. Phylogenetics (8.1) has functioned as a donor of many computational tools (see 5.2, 5.4) to stemmatology. Linguistics (8.2) makes complex genealogical judgements just as stemmatology does, albeit with a focus on language as a whole, not on a single work. Anthropological phylomemetics (8.3; an umbrella term proposed by ","PeriodicalId":338644,"journal":{"name":"Handbook of Stemmatology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134237006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-07DOI: 10.1515/9783110684384-006
J. Zundert
This chapter may well be the hardest in the book for those that are not all that computationally, mathematically, or especially graph-theoretically inclined. Textual scholars often take to text almost naturally but have a harder time grasping, let alone liking, mathematics. A scholar of history or texts may well go through decades of a career without encountering any maths beyond the basic schooling in arithmetic, algebra, and probability calculation that comes with general education. But, as digital techniques and computational methods progressed and developed, it transpired that this field of maths and digital computation had some bearing on textual scholarship too. Armin Hoenen, in section 5.1, introduces us to the early history of computational stemmatology, depicting its early beginnings in the 1950s and pointing out some even earlier roots. The strong influence of phylogenetics and bioinformatics in the 1990s is recounted, and their most important concepts are introduced. At the same time, Hoenen warns us of the potential misunderstandings that may arise from the influx of these new methods into stemmatology. The historical overview ends with current and new developments, among them the creation of artificial traditions for validation purposes, which is actually a venture with surprisingly old roots. Hoenen’s history shows how a branch of computational stemmatics was added to the field of textual scholarship. Basically, both textual and phylogenetic theory showed that computation could be applied to the problems of genealogy of both textual traditions and biological evolution. The calculations involved, however, were tedious, error-prone, hard, and cumbersome. Thus, computational stemmatics would have remained a valid but irksome way of dealing with textual traditions if computers had not been invented. Computers solve the often millions of calculations needed to compute a hypothesis for a stemma without complaint. They do so with ferocious speed and daunting precision. But it remains useful to appreciate that this is indeed all they do: calculate. The computer – or algorithm – does not have any grasp of the concepts or problems that it is working on. Nowhere in the process leading from variant data to a stemmatic hypothesis does any software or hardware realise that it is working on a textual tradition or genetic material. It has no feelings about that work and – more saliently – is indifferent to the quality, correctness, or meaning of the result it calculates. It is especially for this last reason that textual scholars should take note of the methods and techniques involved in calculating stemmata, even if the maths may not always be palatable work. Computer code and chips process data and yield some result or other. None of the nouns in the previous sentence somehow becomes inherently neutral, objective, and correct by virtue of being digital or mathematical in nature. If an algorithm contains a calculation error, the computer will repe
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Pub Date : 2020-09-07DOI: 10.1515/9783110684384-003
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Pub Date : 2020-09-07DOI: 10.1515/9783110684384-010
{"title":"Terminology in other languages","authors":"","doi":"10.1515/9783110684384-010","DOIUrl":"https://doi.org/10.1515/9783110684384-010","url":null,"abstract":"","PeriodicalId":338644,"journal":{"name":"Handbook of Stemmatology","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122928791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-07DOI: 10.1515/9783110684384-013
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Pub Date : 2020-09-07DOI: 10.1515/9783110684384-004
The elaboration of a stemma codicum, representing the filiation between the witnesses that transmit a text whose original is lost, is the core of the genealogical method: on the one hand, only once these relationships have been determined can text restoration be tackled; on the other hand, the stemma may be the goal of the work of synthesising a certain textual tradition. In order to construct a stemma, some preliminary steps are needed; these steps are specifically treated in the sections of the present chapter. The first step of the stemmatic workflow – namely, the identification of both direct and indirect witnesses (technically: heuristics) – is the subject of Gabriel Viehhauser’s contribution (3.1). After sketching a brief history of the concept, he addresses the issue of how the heuristic process is carried out after the material turn in the twentieth century, providing useful information about both the traditional and the more recent tools that researchers have at their disposal. Particularly relevant is the advent of digital catalogues and digital facsimiles, which can offer easier and faster access to primary sources. This development has profound consequences for framing the history of transmission of a text, as shown in the critical review of various Parzival editorial projects based on different heuristic approaches. Caroline Macé (3.2) deals with a frequently neglected aspect of editorial practice: the use of the indirect tradition of a given text (e.g. translations and rewritings, quotations, interpolations, glosses, and marginal notes) for stemmatological purposes. The conclusion reached, namely that “the main point of using indirect witnesses is that their text has been preserved ‘outside’ of the main tradition; they can therefore be used as an ‘outgroup’ [...] to orientate the stemma”, is central from a methodological point of view. The indirect tradition can also be used to document the early history of textual traditions – especially when indirect witnesses are older than the oldest extant direct ones of a given work – as well as the appearance of (hyp)archetypes. Despite their relevance for stemmatic analysis, she warns us to use indirect witnesses with great caution due to the methodological difficulties inherent to them. In her section (3.3), Tara Andrews addresses the problems of transcribing and then comparing (technically: collating) the different instances of a text preserved in several witnesses. In so doing, she presents both non-digital and digital ways of transcribing and collating witnesses, providing also some insights into the current theoretical debate on what these processes and the results they produce mean to different scholars and scholarly communities. She offers a definition of the central notion of a “variant location”, which arises when different witnesses show different readings at a point that can be considered “the same place” in the text. The discovery of these places is key to the establishment of a
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