Anger and Apology, Recognition and Reconciliation: Managing Emotions in the Wake of Injustice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it