Creating into the Future: Speculative Practice across Borders, Disciplines, and Knowledges
Bibliographic record
Abstract
In the next decade, according the Intergovernmental Panel on Climate Change, we are going to see increasing climatic events with compounding and unprecedented economic, social, and political impacts which will affect livelihoods and ways of life. To borrow Margaret Atwood’s declaration, we are no longer talking about climate change, we are facing “ everything change” including how we practise as artists in various institutional contexts at home and abroad. It is plausible that in our lifetime we will see our venues, galleries, studios, audiences, and other platforms where we create, tour, and present affected. Therefore, we are in a critical time as practitioners to reconsider our capacities and voices, how we untether from our ways of conventionally working within and across institutional structures, and how we can adapt to the challenges ahead. This autobiographical essay reflects on my own pluralities of experiences for leaving Canada and rationale for staying abroad; and how through my practice a decade later, I am considering what returning might mean. My approach is to situate this reflection within a provocation that is speculative around being a practitioner in the future and how working across borders, disciplines, and orientations can prepare one to be adaptive and responsive in the turbulent times ahead.
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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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.079 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".