Joker Exchange Online - Meeting The Risks and Opportunities of the COVID-19 Crisis
Bibliographic record
Abstract
In this article, we narrate and analyse patterns of engagement and harvest key learning from the Joker Exchange Online (JEO) events on 11th April and May 2nd. We map the impact of these online events to inform future events as an effective collective response/strategy to global challenges. At the same time, we are Theatre of the Oppressed practitioners who attended/presented in the JEO, and this informs our research and engaged interest in theatre and community work on the margins of theatre, activism, and social change. The article has three parts: the first part look at the “triggers” for the Joker Exchange Online and its aims; the second part reports and analyses testimonies from JEO participants; the third part outlines key learnings, suggestions, and considerations for similar future global online events. Appendices show a full schedule of sessions, a list of videos made at JEO, and share testimonials.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".