Bibliographic record
Abstract
This article provides a much needed inquiry into the legacy of slavery from an interdisciplinary perspective, including the historical, socioeconomic, political, and the epistemic. It makes an important distinction between the legacy of slavery and its persisting damages. By investigating this legacy’s effects on peoples, communities, and societies, it highlights the imperative of situating the pains and sufferings of historical traumas within contemporary structural oppression and institutional discrimination that have perpetuated these harms. The article consists of four sections: it first outlines the legacy of slavery, comprised in instrumentalizing black bodies for economic gains, employing political aggression to colonize both lands and minds, applying racialized discourse to demean and dehumanize, and oppressing people of African descent through structural violence. It then discusses the legacy’s injuries as transgenerational and cultural traumas, and how these wounds are experienced by the relevant communities. The third section focuses on racism as a significant harm, analyzing different forms of racism (internalized, interpersonal, and institutional) as interconnected and mutually reinforcing. To conclude, this article considers challenges in addressing the legacy of slavery and puts forward tentative ideas for collective healing.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".