Building a Digital Research Community in Medieval and Early Modern Studies: The Australian Network for Early European Research
Bibliographic record
Abstract
This paper examines the work done by the Australian Network for Early European Research (NEER) to build a national digital research community in this field. Funded through the Australian Research Council's Research Networks programme during the period 2005-2010, NEER's overall goal was to enhance the scale and focus of Australian research in medieval and early modern studies. Developing and implementing appropriate digital technologies was one of the main methods used to address this goal. In the end, NEER's digital programme produced three main services: a service for collaboration (Confluence), a service for the publication and storage of research outputs (PioNEER), and a service for identifying and engaging with the objects of this research (Europa Inventa). This paper evaluates the effect of these services on Early European research in Australia. It also considers their future, now that government funding for NEER has ended.
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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 | 0.018 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".