Narrating the Constitution: Exploring the Role of Narrative in Constitution-Making in Divided Societies
Bibliographic record
Abstract
Constitution-making in societies divided along identitarian lines is challenging, particularly in finding conceptions of the polity around which divided groups can coalesce. Modern liberal theories of constitution-making in such circumstances (such as those of Andrew Arato, Hanna Lerner and Madhav Khosla) propose that the process can create cohesive identities. But these models focus on the role of political institutions and ideas, and ignore how individual citizens perceive, affect and are affected by the process. This thesis uses South Africa’s transition from apartheid to democracy to demonstrate that (1) narrative processes (as storytelling and a mechanism by which to contextualise and understand events) were present during that transition; and (2) the models under-theorise narrative’s role helping divided societies to cohere. Consequently, I propose that a model of narrative identity formation might usefully assist in understanding the role of perceptual and affective aspects of constitution-making processes to overcome identitarian dissensus.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".