Critica del manoscritto base. Pairwise and multiple alignment for the automatic collation.
Bibliographic record
Abstract
The aim of this paper is to analyse the reasons and the consequences of the use of a base-witness for the collation, in relation with the major textual scholarship traditions. Using a base-witness means collating each witness against the same one, i.e. the base. We shall focus on collation practices, and in particular on digital collation; three classes of algorithms have been used for this purpose: pairwise alignment, progressive multiple alignment and non-progressive multiple alignment. The results obtained using these different procedures will be shown and compared. In line with recent contributions (among which Sculley and Pasanek; Burdick et al., 101; Pierazzo, 110), this paper argues that the use of digital techniques should come with a critical understanding of the mechanisms informing those techniques, in order to better choose among them and to demystify the feeling of objectivity and truth coming from the result of a computational process; engaging on explicit ground with the digital side of Digital Humanities in general, and digital collation in particular, eventually means recognizing and promoting the interpretative nature of any investigation in the Humanities. Bibliography Andrews, Tara. “Digital Techniques for Critical Edition.” In Armenian Philology in the Modern Era: From Manuscript to Digital Text, Ed. V. Calzolari and M. E. Stone. Leiden: Brill, 2014. Burdick, Anne, Johanna Drucker, Peter Lunenfeld, Todd Presner, and Jeffrey Schnapp. Digital_Humanities. Cambridge, MA: The MIT Press, 2012. Dekker, Ronald Haentjens, Dirk van Hulle, Gregor Middell, Vincent Neyt, and Joris van Zundert. “Computer-Supported Collation of Modern Manuscripts: CollateX and the Beckett Digital Manuscript Project.” Digital Scholarship in the Humanities 30, no. 3 (September 1, 2015): 452–70. Dekker, Ronald Haentjens, and Gregor Middell. “Computer-Supported Collation with CollateX”. 2011. Froger, Jacques. La Critique des textes et son automatisation, Dunod 1968. Pierazzo, Elena. Digital Scholarly Editing: Theories, Models and Methods. Ashgate, 2015. Robinson, Peter. “Collate 2, and the design for its successor: CollateXML (now, CollateX)”, 2007. Schmidt, Desmond Allan. “Merging Multi-Version Texts: A General Solution to the Overlap Problem.” In Proceedings of Balisage: The Markup Conference 2009, Vol. vol. 3. Balisage Series on Markup Technologies. Montréal, Canada, August 11 - 14, 2009, 2009. Schmidt, Desmond, and Robert Colomb. “A Data Structure for Representing Multi-Version Texts Online.” Int. J. Hum.-Comput. Stud. 67, no. 6 (June 2009): 497–514. Sculley, D., and Bradley M. Pasanek. ‘Meaning and Mining: The Impact of Implicit Assumptions in Data Mining for the Humanities’. Literary and Linguistic Computing 23, no. 4 (1 December 2008): 409–24. Spencer, Matthew, and Christopher J. Howe. “Collating Texts Using Progressive Multiple Alignment.” Computers and the Humanities 38, no. 3 (2004): 253–70.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".