Enacting Truth and Reconciliation Through Community-University Partnerships
Bibliographic record
Abstract
Truth and reconciliation efforts around the world demonstrate distinctive cultural approaches, motivations, and outcomes. Utilizing four international cases of truth and reconciliation in Canada, South Africa, Germany and South Korea, we first establish common processes in national or macro-level truth and reconciliation as a result of past atrocities. In the U.S., 4000+ documented racial terror lynchings took place between the years 1870-1950. In the absence of a national truth and reconciliation commission for racial terror lynchings in the U.S., we developed and applied a micro-level model and practices outlined by the Equal Justice Initiative to advance truth and reconciliation at the grassroots level, fueled by community-university partnerships. In this paper we detail components of our community-university partnership model that might allow communities across the United States to advance grassroots efforts in their own local context. We note that truth and reconciliation is an ongoing process that includes both macro (national) and micro (grassroots) level approaches rather than an outcome that will satisfy all stakeholders effected by the events.
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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".