Anger and Apology, Recognition and Reconciliation: Managing Emotions in the Wake of Injustice
Bibliographic record
Abstract
Abstract This article treats rituals of apology and reconciliation as responses to social discontent, specifically to expressions of anger and resentment. A standard account of social discontent, found both in the literature on transitional justice and in the social theory of Axel Honneth, has it that these emotional expressions are evidence of an underlying psychic need for recognition. In this framework, the appropriate response to expressions of anger and discontent is a recognitive one that includes victims of injustice in the political community by showing them that they are valued members. In the aftermath of injustices, such recognitive responses are thought to include acknowledgments of victim suffering, reconciliatory gestures, and rituals of contrition. I will argue, against this narrative, that treating victim anger as evidence of an underlying need for recognition threatens to depoliticize emotional responses to injustice by treating them as symptoms of psychic injuries instead of intelligible political claims. Discussing mainly the Canadian Truth and Reconciliation process set up to deal with the history of the Indian Residential School system, I show how rituals of reconciliation and apology, in the context of settler-colonial states and neoliberal politics, serve as a biopolitical management of “bad” emotions. This will serve as a critique both of the politics of reconciliation and of social–theoretic approaches that treat expressions of discontent exclusively through a lens of recognition. Instead, I argue, in politics as well as theory, we need to engage with emotional expressions as intelligible political claims that exceed the psychic need for recognition.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".