New perspectives on healing collective trauma: towards social justice and communal well-being
Bibliographic record
Abstract
This book examines how historical injustices, especially enslavement, colonialism, and systemic racism, continue to impact societies today. Drawing on global case studies, from the legacies of transatlantic slavery and colonialism in the Americas, to indigenous experiences of reconciliation in Canada, racial healing initiatives in the US, and community intergenerational dialogues in Africa, it explores how past traumas are transmitted across generations, shaping contemporary inequalities. The authors argue that addressing these enduring harms requires collective healing, involving processes of acknowledging the wounds, truth-telling, reparations, reconciliation, and inclusive dialogue across diverse generations and communities. Innovative frameworks presented include “Emotional Justice”, which emphasises relational well-being and narrative transformation, and “intergenerational dialogue and inquiry” that re-affirms human dignity and restores traditional wisdom and communal resilience. The book also introduces ideas of "healing architecture" and “politics of dignity’ that outlines structural features of just society, showing how institutions and can be intentionally designed to respect equal intrinsic value of all persons, nurture social justice, and foster collective well-being. Gathering interdisciplinary perspectives and renowned global scholars in one volume, the book offers practical strategies and hopeful narratives that demonstrate how societies can move from entrenched division towards communal healing and shared flourishing. It is an essential resource for anyone interested in creating more just, empathetic, and inclusive societies in our increasingly interconnected world.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".