T<scp>he</scp> A<scp>mbiguous</scp> R<scp>ole of</scp> R<scp>eligion in the</scp> S<scp>outh</scp> A<scp>frican</scp> T<scp>ruth and</scp> R<scp>econciliation</scp> C<scp>ommission</scp>
Bibliographic record
Abstract
This article examines the ambiguous role that religion, particularly Christianity, played in the South African Truth and Reconciliation Commission (TRC) and in South Africa's transition from apartheid to democracy. On the one hand, religious‐symbolic discourse was an empowered truth‐telling discourse used by victims and survivors in recounting their stories of apartheid abuse. Moreover, it was a discourse publicly affirmed and encouraged by TRC leaders such as Desmond Tutu. On the other hand, religious discourse was prohibited for perpetrators who came forward seeking amnesty; for amnesty applicants, only a legal‐forensic mode of truth‐telling was authorized by commissioners. We argue that this tension between religious and legal discourse in the TRC has contributed to the establishment of a democratic political culture in South Africa; yet, at the same time, it has also contributed to delays in social and economic justice for victims and survivors.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.003 |
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".