NextGen Meets OldGen: Encoding an Eighteenth-Century Metadata Project
Bibliographic record
Abstract
This poster, stemming from the "Networking Jacobites" project at Simon Fraser University, reports on the challenges faced and theoretical perspectives gained in our attempts at describing an 18th-century Jacobite manuscript, “The Lyon in Mourning.” Compiled by Jacobite Episcopalian minister Robert Forbes after the suppression of the 1745 Jacobite Rising, “The Lyon in Mourning'' consists of 2148 pages of transcribed conversations, narrative accounts, poems, songs, letters, epitaphs, and even material relics such as scraps of fabric and pieces of a boat. It is an extraordinary document in Scottish history, compiled to preserve the memories of those on the losing side of the Jacobite conflict at a time when Jacobites were subject to legal proscription including capital punishment. It is also a remarkable document in the history of text encoding: not only do the ten-volumes serve as an early "hypertext" with various cross-references and footnotes throughout the collection, but Forbes' meticulously compiled manuscript also seems to anticipate the methodological protocols of the TEI, featuring careful documentation on the biographical, bibliographical, and presentational metadata about his source material. Our poster will discuss how the project has approached encoding Forbes' 10-volume manuscript and the difficulties that we have faced during the first phase of the project, which has focused on compiling metadata for each item. This metadata on the people, places, and places found in the pages of Forbes' manuscript—encoded using elements drawn from the "msdescription" and "corpus" modules of the TEI—will allow our project to derive new evidence about the extent and nature of Jacobite activity in post-1745 Scotland, despite government attempts to suppress the Jacobite cause. However, as we discuss, using TEI to analyze the work of someone who has already done his own work of encoding becomes a difficult task in itself. How do we encode the encoding?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".