Speaking Justice, Performing Reconciliation: Twin Challenges for a Postcolonial Ethics
Bibliographic record
Abstract
This article is concerned with problems of injustice that arise from law’s emphasis on the status quo. I contend that a commitment to social justice means that we will (at least sometimes) need to improvise processes and institutions within the broader political community, rather than relying on law to bring about justice. I explore the efficacy and pitfalls of these kinds of improvised justice movements—undertaken within an extra-legal social sphere—with particular attention to the possibilities of truth and reconciliation commissions. In the first part, this exploration takes the form of a comparative analysis of two different national orientations to the question of postcolonial justice. I consider truth and reconciliation commissions more closely in the second part: the sense in which they can coherently be seen as improvised justice, the ways in which they may be inadequate vehicles, or even distortions, of justice, and the possibility that these potential defects can be overcome through behaviours that reveal an ‘ethos of improvisation’. As I think that performativity is a common element of both musical improvisation and truth and reconciliation commissions, I aim to show that this ethos is a common feature of both processes, albeit perhaps with some modifications, and that it addresses concerns about the limitations of extra-legal attempts at justice.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.217 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".