Compare methods

	Stemmatic Textual Criticism	Computational Stemma Reconstruction
Field	Religious Studies	Religious Studies
Family	Process / pipeline	Machine learning
Year of origin	1958	2009
Originator	Karl Lachmann (codified by Paul Maas; refined by M. L. West)	Adapted from biological phylogenetics (Howe, Robinson, O'Hara); benchmarked by Roos & Heikkilä
Type	Genealogical pipeline for reconstructing a lost archetype from manuscript witnesses	Algorithmic tree-inference pipeline for reconstructing manuscript genealogies
Seminal source	Maas, P. (1958). Textual Criticism (trans. B. Flower). Oxford: Clarendon Press. ISBN: 9780198143185	Roos, T., & Heikkilä, T. (2009). Evaluating methods for computer-assisted stemmatology using artificial benchmark data sets. Literary and Linguistic Computing, 24(4), 417-433.
Aliases	Lachmannian Method, Recensionism, Genealogical Textual Criticism, Stemmatics	Phylogenetic Stemmatology, Computer-Assisted Stemmatology, Algorithmic Stemma Building, Cladistic Textual Criticism
Related	4	4
Summary	Stemmatic textual criticism, the method codified by Karl Lachmann and given its classic formulation by Paul Maas in 1958, reconstructs the lost original of a work transmitted in many handwritten copies. Because every act of copying introduces errors, manuscripts that descend from a common defective ancestor share those errors. Maas's insight is that shared errors, not shared correct readings, reveal genealogy: by grouping witnesses according to the significant errors they hold in common, the critic builds a stemma codicum, a family tree of manuscripts rooted in the archetype. M. L. West's 1973 handbook turned these principles into working editorial practice for Greek and Latin texts, including the scriptures transmitted in those languages. The pipeline runs from collation through error analysis to a reconstructed archetype that can be defended reading by reading.	Computational stemma reconstruction borrows the mathematics of biological phylogenetics to rebuild the family tree of a manuscript tradition automatically from coded variant readings. Each surviving witness is treated as a taxon and each place of textual variation as a character with discrete states, exactly as a biologist treats species and the genes that vary among them. Tree-inference algorithms then search for the genealogy that best explains the observed pattern of variants, typically the tree requiring the fewest reading changes (maximum parsimony) or the most probable tree under an evolutionary model. Teemu Roos and Tuomas Heikkilä's 2009 study established how to evaluate these methods rigorously, building artificial manuscript traditions with a known true stemma and measuring how accurately each algorithm recovered it. The result is a scalable, reproducible complement to the hand-built Lachmannian stemma.