Truth in politics : rhetorical approaches to democratic deliberation in Africa and beyond
Bibliographic record
Abstract
Democracy is about competing "truths". This is why "rhetoric"- the study of public deliberation and the training in public debate and argumentation - is part of democracy in development. This volume acclimatizes "rhetoric" to the philosophical scene in South Africa, and more in general in Africa as a whole, and reflects on the emergence of public deliberation in the South African democracy through a reading of the 1995-1998 Truth and Reconciliation Commission (TRC) in terms of Aristotelian rhetoric. Four papers (part 1) tackle, from four different angles, the re-telling of private truths about a public regimen of affairs in front of the TRC. In Part 2, public deliberation and the fashioning of truth are approached from a variety of perspectives, examples and situations of "rhetorical democracy" from elsewhere in Africa (Nigeria) and beyond. Part 3 offers examples of how rhetoric may be brought to bear upon politics in order to understand how dialogue between different levels of agency creates democratic negotiation and, in the process, shapes policy, as for example in the case of the African Renaissance, the land redistribution programme in postapartheid South Africa and the 1991 National Conference of Congo-Brazzaville. The volume closes on a philosophical analysis of the "ethical" dimension inherent to public deliberation as well as to the contest of beliefs, and on an examination of the volume's contents in the light of long-standing concerns of African philosophy and of the journal 'Quest'. Contributors: Charles Calder, Barbara Cassin, Mary Jane Collier, Erik Doxtader, Eugene Garver, Yehoshua Gitay, Lisa Hajjar, Darrin Hicks, Johnson Segun Ige, Abel Kouvouama, Andrea Lollini, Reingard Nethersole, Sanya Osha, Philippe-Joseph Salazar, Lydia Samarbakhsh-Liberge, Wim van Binsbergen, Charles Villa-Vicencio. [ASC Leiden abstract]
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".