Collective Healing to Address Legacies of Transatlantic Slavery: Opportunities and Challenges
Bibliographic record
Abstract
In this article, we show how pathways to justice and reconciliation pertaining to the transatlantic slavery should begin with collective healing processes. To illustrate this conclusion, we first employ a four-fold conceptual framework for understanding collective healing that consists in: (1) acknowledging historical dehumanizing acts; (2) addressing the harmful effects of dehumanisation; (3) embracing relational rapprochement; and (4) co-imagining and co-creating conditions for systemic justice. Based on this framework, we then examine existing collective healing practices in different contexts that are aimed at addressing legacies of transatlantic slavery. In doing so, we further identify challenges and pose critical questions concerning such practices. While globally there are, and have been, many different kinds of racism and slavery, and even though transatlantic slavery has many features specific to it, nevertheless, we hope that this exploration of collective healing will be illuminating for other situations where acts of brutality have served to demean and dehumanize.
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 | 0.026 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".