Learning from Greensboro: Truth and Reconciliation in the United States, Lisa Magarrell and Joya Wesley
Bibliographic record
Abstract
Why should a city in the US look to emerging or reforming democracies in other parts of the world to find ways to come to terms with its troubled history? By telling, in a straightforward, readable manner, the interesting story of the Greensboro Truth and Reconciliation Commission (GTRC), Lisa Magarrell and Joya Wesley stimulate the reader to reflect on this question and its implications for the use of truth and reconciliation commission processes to examine past racially based, egregious violations of human rights in developed, stable democracies. They also provide their own thoughtful analysis, extracting ‘lessons learned,’ assessing success and comparing the GTRC and its context with other truth and reconciliation commissions. As participants, Magarrell and Wesley are clearly advocates of the GTRC process, but any partisanship is countered by a balanced effort to acknowledge its weaknesses. The book is a valuable contribution to the study of truth and reconciliation commissions as potential tools for achieving social justice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".