Black Lawyers, White Courts: The Soul of South African Law
Bibliographic record
Abstract
In the struggle against apartheid, one often overlooked group of crusaders was the coterie of black lawyers who overcame the Byzantine system that the government established oftentimes explicitly to block the paths of its black citizens from achieving justice. Now, in their own voices, we have the narratives of many of those lawyers as recounted in a series of oral interviews. Black Lawyers, White Courts is their story and the anti-apartheid story that has before now gone untold. Professor Kenneth Broun conducted interviews with twenty-seven black South African lawyers. They were asked to tell about their lives, including their family backgrounds, education, careers, and their visions for the future. In many instances they also discussed their years in prison or exile, or under house arrest. Most told of both education and careers interrupted because of the ongoing struggle. The story of the professional achievements of black lawyers in South Africa -- indeed their very survival -- provides an example of the triumph of individuals and, ultimately, of the law. Black Lawyers, White Courts is about South Africa, and about black professionals in that country, but the lessons its protagonists teach extend far beyond circumstances, geography, or race.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.023 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".