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Record W2586076898 · doi:10.1111/hypa.12314

Losing Hope: Injustice and Moral Bitterness

2017· article· en· W2586076898 on OpenAlexfundno aff
Katie Stockdale

Bibliographic record

VenueHypatia · 2017
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaKillam Trusts
KeywordsInjusticePoliticsEnvironmental ethicsSocial psychologyMoral disengagementPsychologyAngerSociologyPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

In this article, I defend a conception of bitterness as a moral emotion and offer an evaluative framework for assessing when instances of bitterness are morally justified. I argue that bitterness is a form of unresolved anger involving a loss of hope that an injustice or other moral wrong will be sufficiently acknowledged and addressed. Orienting the discussion around instances of bitterness in response to social and political injustices, I argue that bitterness is sometimes morally justified even if it is ultimately undesirable to bear. I then suggest that focusing only on the harms and risks of bitterness can distract from its positive role as a moral reminder about a past or persistent injustice, indicating that there is still moral and often political work left to do. Finally, I address the concern that bearing bitterness may lead to despair and inaction. I respond by arguing that moral agents can and do persist in their moral and political struggles with bitterness, and without hope that their efforts will be successful.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.014
Scholarly communication0.0040.003
Open science0.0000.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.372
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations36
Published2017
Admission routes1
Has abstractyes

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