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Record W7075648533

Anger and apology, recognition and reconciliation: managing emotions in the wake of injustice

2022· article· en· W7075648533 on OpenAlexaboutno aff

Bibliographic record

VenuePhilPapers (PhilPapers Foundation) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
FundersUniversity of Oxford
KeywordsInjusticeAngerPsychicPoliticsSocial injusticeContext (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

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.\n\n

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.069
GPT teacher head0.222
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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