Islandora and TEI: Current and Emerging Applications/Approaches
Bibliographic record
Abstract
Islandora is an open-source software framework developed since 2006 by the University of Prince Edward Island's Robertson Library. The Islandora framework is designed to ease the management of security and workflow for digital assets, and to help implementers create custom interfaces for display, search, and discovery. Turnkey options are provided via tools and modules ("solution packs") designed to support the work of a particular knowledge domain (such as chemistry), a particular content type (such as a digitized newspaper), or a particular task (such as TEI encoding). While it does not yet have native support for TEI, Islandora provides a promising basis on which digital humanities scholars could manage the creation, editing, validation, display, and comparison of TEI-encoded text. UPEI's IslandLives project, with its forthcoming solution pack, provides insight into how an Islandora version 6 installation can support OCR text extraction, automatic structural/semantic encoding of text, and web-based TEI editing and display functions for site administrators. This article introduces the Islandora framework and its suitability for TEI, describes the IslandLives approach in detail, and briefly discusses recent work and future directions for TEI work in Islandora. The authors hope that interested readers may help contribute to the expansion of TEI-related services and features available to be used with Islandora.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".