Bibliographic record
Abstract
The authors propose a conception of national reconciliation based on the building or rebuilding of trust between parties alienated by conflict. It is by no means obvious what reconciliation between large groups of people amounts to in practice or how it should be understood in theory. Lack of conceptual clarity can be illustrated with particular reference to postapartheid South Africa, where reconciliation between whites and blacks was a major goal of the Mandela government and the Truth and Reconciliation Commission. The authors argue that a conception of reconciliation in terms of trust offers a promising solution to prominent conceptual confusions surrounding the notion of “national reconciliation” or reconciliation between large groups. By emphasizing the centrality of contextually variable trust in viable relationships, the authors accommodate an emphasis on human relationships and attitudes, as stressed by Desmond Tutu. They argue, however, against any simplistic application of purely individualistic or spiritual concepts to large groups and institutional contexts.
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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".