Doing justice: Moving from the pain and trauma of injustice to healing
Bibliographic record
Abstract
Injustice lies at the heart of many societal challenges. By adopting the lens of injustice, we argue that critical insights and interventions can be illuminated. We highlight the importance of healing for addressing the pain and trauma of injustice as well as the role of justice in the healing process, where it can serve as a motivating force (e.g., when people desire justice), healing salve (e.g., when people “do justice”), and desired end state (e.g., working towards a just society). In doing so, we outline how to facilitate healing from injustice and enable the transition from injustice to justice. We provide an agenda for future research that showcases the importance of further understanding the pain and trauma of injustice. We conclude with a call for scholars and practitioners to engage in courageous action to recognize the toll of injustice, promote healing, and work towards a more just society.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".