Towards a Black Love and Care Ethic: Reimagining Social Work Through Black Technologies
Bibliographic record
Abstract
The insidious impact of anti-Black racism on Black peoples remain obscured by even the most well-meaning Diversity, Equity, and Inclusion (DEI) programming and curricula. The following inquiries guide this article: What becomes possible, in clinical settings and beyond, when the needs, dreams, and abundance of Blackness and Black peoples are tended to and affirmed? What Black technologies can be employed to cultivate the fullness of this possibility? This article will introduce readers to the Black Love and Care (BLaC) Ethic, a practical framework intended to disrupt the impacts of anti-Black racism and its intersecting oppressions by shifting how clinical practitioners practice being with Blackness. The BLaC Ethic is an invitation into a worldbuilding practice that explores what is possible when systems tend to and affirm Black experiences. The article will apply The BLaC Ethic through a reimagined clinical social work lens. The BLaC Ethic will also be explored from the perspective of “black technologies,” or methods of perspectivity developed by Black peoples, such as the Afrocene, Endarkened Storywork Epistemology, and the Divine Genders Oracle Deck.
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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 | 0.042 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.040 | 0.126 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".