This article reviews some of the digital tools currently available for reconstructive textual editing. First the main idea of reconstructive textual editing is summarised, then its steps amenable to algorithmic description are compared to similar ones in evolutionary biology. The the unequal ability of its variants to be relationship revealing is an important difference between the two fields. Two Latin texts with a complicated transmission are then introduced and used as data to illustrate some available tools in praxi. The main focus is on stemma reconstruction. Some steps of the process can already be largely automated, especially collating texts. On the whole it is found that tree-constructing software is of little help in the case of the medical text Liber Aurelii, whereas it is somewhat more helpful for Plato of Tivoli’s translation of the Centiloquium. In a concluding part, the main problems for algorithmic approaches to the stemma are discussed: incomplete witnesses leading to only partly overlapping text samples, contamination in some witnesses, and rooting the automatically generated trees.
This study is devoted to two of the oldest known manuscripts in which the
oeuvre of the medieval mystical author Hadewijch has been preserved: Brussels,
KBR, 2879-2880 (ms. A) and Brussels, KBR, 2877-2878 (ms. B). On the basis of
codicological and contextual arguments, it is assumed that the scribe who
produced B used A as an exemplar. While the similarities in both layout and
content between the two manuscripts are striking, the present article seeks to
identify the differences. After all, regardless of the intention to produce a
copy that closely follows the exemplar, subtle linguistic variation is
apparent. Divergences relate to spelling conventions, but also to the way in
which words are abbreviated (and the extent to which abbreviations occur). The
present study investigates the spelling profiles of the scribes who produced
mss. A and B in a computational way. In the first part of this study, we will
present both manuscripts in more detail, after which we will consider prior
research carried out on scribal profiling. The current study both builds and
expands on Kestemont (2015). Next, we outline the methodology used to analyse
and measure the degree of scribal appropriation that took place when ms. B was
copied off the exemplar ms. A. After this, we will discuss the results
obtained, focusing on the scribal variation that can be found both at the level
of individual words and n-grams. To this end, we use machine learning to
identify the most distinctive features that […]
