“Parity of Esteem”: A Conceptual Framework for Assessing Peace Processes, with a South African Case Study
Bibliographic record
Abstract
In the first part of this article a conceptual framework for assessing the durability of negotiated peace settlements is developed. The framework elaborates on approaches that hold that dealing with issues of relative group status is central to the effectiveness of negotiated settlements. The dynamics of post-settlement competition, the negotiated rules that shape such competition, and the impact of competitive outcomes on inter-group status, whether adverse or positive, is explored. It is argued that peace settlements with rules that shape competition in such a way that both parity of outcomes and parity of esteem can be achieved will be more durable. Parity of esteem is achieved to the extent that competitive rules inhibit stakeholders from drawing invidious comparisons from competitive outcomes. The second part of the article comprises a case study of South Africa. A descriptive analysis is made of a particular set of rules that emanate from the 1993/96 negotiated settlement. The competitive arena is the employment market and the rules of affirmative action. The case study centers on a ruling in a case brought to the South African Labor Court by the Solidarity Trade Union. The case illustrates the emergence of invidious comparisons, the dynamics of in-group and out-group interaction, the construction and reconstruction of identities, and the shifting equilibrium of relative group status. In conclusion, it is found that the current rules shaping this competitive situation inhibit the emergence of parity of esteem.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".