Pixels & partnerships: digital publishing and co-operative scholarship in Australian and Britsih imperial history
Bibliographic record
Abstract
The efforts of libraries and archival institutions in collecting and curating dispersed collections of original letters, journals, pictorial sources, and realia/artefacts are essential contributions towards research and scholarship. Recent technological advances in digitization offer important new possibilities in the reproduction of documents and images as well as enhanced access to dispersed institutional holdings.\nThe Lachlan & Elizabeth Macquarie Archive (LEMA) is a co-operative inter-institutional website project based at Macquarie University Library, in partnership with key Australian and UK institutions. The aim of the LEMA Project is to create a digital research gateway to the writings of the Lachlan Macquarie (1761-1824), governor of New South Wales from 1810-1821 and his wife Elizabeth (1778-1835), and thereby assist in the study and analysis of their place in Australian and imperial history. The LEMA framework is designed to explore the personal and global contexts of the Macquaries through the use of full-text transcriptions of documentary sources, as well as contributing towards the identification and digital repatriation of personal objects associated with their lives.\nPixels & Partnerships will discuss the history and infrastructure of the LEMA Project and examine how inter-institutional partnerships can be developed through reciprocity and resource sharing. It will assess the sustainability of such projects and discuss the viability and application of recent technologies in the development of digital research projects.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".