Bibliographic record
Abstract
With over a quarter of the world’s languages the Pacific is a particularly good place to focus on how language records can be made accessible. The creation and description of research records has not always been a priority for humanities academics and any records that are created have typically not been provided with good archival solutions. This is despite these records often being of cultural or historical relevance beyond academia. Many academic researchers at the end of their careers despair at the task of making sense of a lifetime’s output of papers, notes, images, and recordings. Our project, the Pacific and Regional Archive for Digital Sources in Endangered Cultures (PARADISEC), a collaboration between the University of Sydney, University of Melbourne, and the ANU, began in 2003 by digitising analogue tape collections and providing sufficient metadata to make them discoverable. These tapes belonged to retired or deceased researchers and would otherwise have been stored in a house or maybe a library, but in both cases are difficult to find and more difficult to access. In this paper I outline how PARADISEC works and how to find information in it. I will show how we provide access to the collections we hold and how that has helped build links with people and agencies in the Pacific. We have partnered with a number of museums and cultural centres to digitise analogue tapes and are working on ways of getting information about the collection to the source communities so that they can find recordings made by their members in the past.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".