Visualizing Fedora-managed TEI and MEI documents within Islandora
Bibliographic record
Abstract
The Early Modern Songscapes (EMS) project [1] represents a development partnership between the University of Toronto Scarborough’s Digital Scholarship Unit (DSU), the University of Maryland, and the University of South Carolina. Developers, librarians and faculty from both institutions have collaborated on an intermedia online platform designed to support the scholarly investigation of early modern English song. The first iteration of the platform, launched at the Early modern Songscapes Conference, held February 8-9, 2019 at the University of Toronto’s Centre for Reformation and Renaissance Studies, serves Fedora-held Text Encoding Initiative (TEI) and Music Encoding Initiative (MEI) documents through a JavaScript viewer capable of being embedded within the Islandora digital asset management framework. The viewer presents versions of a song’s musical notation and textual underlay followed by the entire song text. This article reviews the status of this technology, and the process of developing an XML framework for TEI and MEI editions that would serve the requirements of all stakeholder technologies. Beyond the applicability of this technology in other digital scholarship contexts, the approach may serve others seeking methods for integrating technologies into Islandora or working across institutional development environments.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".