DART: a new missile in Australia's e‐research strategy
Bibliographic record
Abstract
Purpose The aim of this paper is to provide a brief overview of the evolution of a new e‐research paradigm and to outline key projects and developments in Europe, North America, Canada and Australia. The article also provides a detailed summary of the Dataset Acquisition, Accessibility and Annotation e‐Research Technology (DART) project. Design/methodology/approach A review of relevant government reports, documents and general literature was conducted. Findings Projects currently being conducted in Europe, the USA, Canada and Australia are part of an international movement that aims to use modern ICTs to enhance e‐research. The DART project is a significant part of this movement as it has adopted a “whole process” approach to e‐research, and provides a platform for the examination of the technical, legal and policy issues that arise in the new e‐research environment. Originality/value Provides an overview of current projects that concern the development of e‐research, with a particular focus on Australian research and the DART project.
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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".