Bibliographic record
Abstract
This writing illuminates a possible stance for an ethic of justice-doing as a frame for community work and therapy. This approach to justice-doing is offered as an imperfection project, and while incomplete and necessarily flawed, it has been helpful to groups of workers striving to practice more in line with our collective commitments for social justice. This approach is profoundly collaborative and informed by decolonizing practice and anti-oppression activism. I will describe the intentions that guide this stance, which include striving towards centering ethics, doing solidarity, addressing power, fostering collective sustainability, critically engaging with language, and structuring safety. Even an imperfect orientation towards justice-doing can open our work to transformations for ourselves, the people we work alongside, and our communities and society, and offer the potential for experiencing the social diving. This article is framed from a keynote delivered at the Winds of Change Conference held in Ottawa, Ontario in June 2012. I acknowledge the Algonquin people whose territories we met on. Finally, marcela polanco (2011), who describes her work as a therapy of solidarity, will offer a reflection on my position for an ethic of justice-doing.
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 | 0.061 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.183 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".