This week’s LODLIB contains the author’s accepted version of our data paper and related datasets of Harnack’s 1924 reconstruction of the Gospel of Marcion (GMarc). Heartfelt thanks go to the journal’s editor-in-chief, Barbara McGillivray, to the four anonymous reviewers for their patient and thorough feedback, and to Paul Dilley for advising me to submit this work to JOHD, one of the many excellent Open Access journals hosted by Ubiquity Press. Because of them, both the paper and the datasets are far better than what I initially submitted. Their constructive criticism is ultimately what pushed me to develop consistent data normalization standards, both for the Harnack datasets and all other reconstructions of GMarc. These standards will allow for consistent and meaningful Computational Linguistics analysis. The fruits of this work are already evident in our data tabulations and visualizations in our LODLIB (a freshly released sample below) and will become more evident as we submit additional datasets and related papers for peer-review and formal publication. We’ll be sure to share DOIs for the paper and datasets (https://doi.org/10.7910/DVN/5TEA5A) as they are published.
This week’s version initiates data normalization for the study of the Gospel of Marcion in concert with our freshly revised datasets for the fourth round of review of a short data paper and related datasets we have submitted to the Journal of Open Humanities Data, whose Editor-in-Chief is Barbara McGillivray at the Alan Turing Institute at Cambridge. The peer-review process has been wonderful and indeed transformative in my thinking and methodology.
The normalization of GMarc data (transforming past messy/noisy reconstructions into standardized data) will—mark my words—prove the tipping point in the transformation of the scholarly study of the canonical and non-canonical gospel strata into legitimate Data Science. In concert with our new normalization standards and normalized datasets of public domain reconstructions, we also release a slew of data visualizations illustrating the contents and relationships of all past GMarc reconstruction datasets. These visualizations clearly reinforce our scientific hypotheses and proofs that GMarc was in fact the third gospel stratum, based on two sources (the first gospel stratum, Qn, and an early version of Mark).
The age of hagiographical controlling bias and assumptions in Gospel Studies is over. The age of Gospel Data Science is upon us. Scholars can either get on board or get out of the way, but no matter what you do, you can’t stop this.
Today’s upload has updates to several sections. We especially want to draw our readers’ attention to our revised reconstruction of the opening of Marcion’s Gospel in verses 3.1 and 4.31. Most notably, we now restore the word “he appeared” / ἐφάνη. In our view, the preponderance of evidence now supports this updated decision, in part based on the newly released finding by Philip Forness of Goethe-Universität Frankfurt am Main that the quotation about Marcion in British Library, Add. 17215 fol 30-33 can be reliably attributed to Jacob of Serugh. (To Phil: congrats on the recent acceptance of your article for the journal New Testament Studies, and thank you for sharing your article in advance of peer-review and publication and allowing me to make use of it publicly.) Our copious footnotes now include quotations of the primary source texts, including a quotation from Phil’s forthcoming Syriac text and translation. His article (which is already in production) does a great job of walking the reader through the historical debates about this quotation, from Barnes, Zahn, and Harnack to several current scholars.
In other book-related news, we have submitted our Harnack GMarc digital edition datasets (human-readable Greek and lemmatized and morphologically tagged Greek) to the Journal of Open Humanities Data and the JOHD data repository in Harvard’s Dataverse for peer-review. Thank you to Paul Dilley for recommending JOHD and to the journal’s editor-in-chief, Barbara McGillivray, for your responsiveness.