Bibliographic record
Abstract
What do we do when harm is done? Who is responsible for holding accountable those who do harm, and who is responsible for helping survivors heal? Does healing look like those who are harmed returning to a pre-harm state—and if not, what else can healing look like? Is it possible for those who offend to reintegrate into spaces? Does punishment deter harmful behaviour? Is rehabilitation possible? What does it mean for communities to hold offenders accountable? Can the state be a reasonable proxy for a community? This article invites a very broad spectrum of questions, asking how people and arts organizations can position themselves to minimize harm, hold themselves and each other accountable when harm is done, and be open to shifts in processes and culture that create new frameworks for safe, generative, and innovative creativity. I draw on my background as a facilitator, theatre director, and artistic organizational leader to explore a method for grappling with these questions and pursuing transformative justice: container-building. In order to foster safety, and thus the conditions for bold, truthful risk-taking, container-building challenges the ways in which the outside status quo might be brought into a room. “Building a container,” as extrapolated from Training for Change’s direct-education facilitation methodologies, is intentionally constructing an environment that holds the group and its work, making space for conflict (which is inevitable), facilitating safety, and encouraging risk-taking. In this article, I offer a non-exhaustive list of things you can consider in building containers for creative work, from your organizational offices to your rehearsal rooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.054 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".