Understanding Australian public library responses to the COVID-19 crisis:Report and recommendations
Bibliographic record
Abstract
On 24th March 2020 the Prime Minister of Australia declared the immediate closure of libraries across the country as part of the national attempt to slow the rate of COVID-19 infections. This meant over 1,600 public library service points across the country in urban, regional and remote locations, were no longer able to offer services on their premises. This research aimed to explore the response by public libraries across Australia to the COVID-19 crisis. Its findings will assist public libraries in understanding their own roles and performance in a community crisis and will enable them to better prepare for and react to similar crises in the future so that community needs are met as efficiently and effectively as possible. In addition, the research aims to identify possible trends in future service and resource provision resulting from measures put in place during the COVID-19 crisis. It is important to note that the protracted nature of the pandemic has meant that many public libraries across Australia are still facing significant operational challenges. We therefore recognise that examples of innovation and best practice are still emerging, and that in many cases public library staff have yet to be afforded the space and time needed for effective reflection on their response to the crisis.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".