International comparators: How does Australia compare internationally?
Bibliographic record
Abstract
Executive summary This report, produced by Dr Katherine Howard and commissioned by the GLAM Peak Bodies, is based on desk research carried out over the period of April to June 2017. Dr Howard was asked to investigate national strategies for digital access to collections in other parts of the world, identify themes within them, and provide recommendations as to how they might inform digital access strategies at the state, territory and federal level in Australia. It is part of the first stage of the Catalyst-funded Digital Access to Collections project 2016-2017. Key findings Strategies analysed came from Europe (28 in total), Canada and New Zealand. Surprisingly, there was little variation identified in the strategies from Europe. The Canadian and New Zealand strategies, although being of a later date than the European ones, also did not provide much variation. The key findings also form the basis of the recommendations. The key findings are: ○ National strategies for digitisation and preservation are created in response to cultural policies set by Ministers of Culture ○ Use of a Secretariat ○ Competence Centres ○ Preservation of digital materials as a high priority ○ The existence (or development) of a national register of digitisation projects ○ Developing Public-private partnerships (PPPs) for funding
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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 | 0.118 | 0.260 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".