Crossing Worlds: South–North Collaborations as Creative Encounters with Arts, Humanities and Sciences
Bibliographic record
Abstract
The humanities and the arts are increasingly in crisis, yet have never been more needed. To illustrate challenges and possibilities in this crisis, we give examples of collaboration between north and south; and amongst scientists, artists, humanities and legal scholars. As a Canadian law professor and a South African visual arts scholar who co-founded a Johannesburg community art centre, we describe our collaborative work and its lessons. Overall, our work strengthened our convictions that while the humanities are in crisis, they are also in a state of becoming, hope and possibility. We describe two examples: HIV/AIDS prevention work in the early 2000s when artists and humanities resources helped stem the tide of infections in the midst of scientific misinformation about how the disease was spread. Our second example is a seminar abroad for Canadian law students in South Africa. As the responsible faculty members, we integrated arts and humanities pedagogies into the students’ experiences. Students found many encounters unsettling, and arts methods both ameliorated and, in some cases, accented, their discomfort. Discomfort, when normalised and engaged, gave way to curiosity and growth. This work proved useful in addressing broader questions of the role of arts and humanities in future democracies.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.034 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".