14. Sustainability, Living Labs and Repair
Bibliographic record
Abstract
The year 2020 started with a massive bushfire crisis in south eastern Australia, resulting in disruption to many communities, the loss of lives and businesses, an estimated loss of a billion animals and the dirtiest air on the planet in the cities of Sydney, Newcastle and Canberra. With record-high temperatures and a punishing draught lasting several years, the Australian bush was primed to explode into flames. With lightning strikes in national parks, the spontaneous eruptions of bushfire spread from the north coast to the south and inland towards the alpine regions of New South Wales and Victoria. With the very hot year of 2019 affecting other parts of the planet in 2020, the Antarctic Peninsula reached a record 65 degrees Fahrenheit. The chapter that follows reflects the new progressive politics of climate change that emerged in 2019 with large mass demonstrations taking place in Australia and around the world and examines the critical role of universities in the mitigation of climate catastrophe. The following interventions are variably focused on the concept of ‘Living Labs’ where thinking is developed within a problem-solving ethos. The three contributions here offer ways to think about sustainability with specific reference to waste recovery, environmental awareness in urban settings and the contribution that a ‘repair’ mentality can make to a shared and re-cycled economy. With a clear-eyed recommendation that mitigation of climate change starts locally, the premise of the paper is that people can work with what is available as local solutions to specific problems. The impact of this approach can be essential to people who sense the impending catastrophe and who may have experienced the crisis directly through compromises in their health outcomes, the experience of trauma and the loss of property and livelihoods, though through no fault of their own. The links through the Western Sydney University campus, common ground to the authors to both its small bushland outpost and further to the local community it serves, suggest that the boundaries of the campus are permeable – and that Living Labs are both a means and metaphor for thinking about how the campus opens learning and knowledge creation about sustainability for its students, staff and community constituents.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.019 |
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".