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Record W4417315573 · doi:10.26556/jesp.v30i7.3854

Reframing Epistemic Partiality: A Case for Acceptance

2025· article· en· W4417315573 on OpenAlexaff
Laura K. Soter

Bibliographic record

VenueJournal of Ethics and Social Philosophy · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsYork University
Fundersnot available
KeywordsCognitive reframingRationalityAnalogyPhilosophy of scienceDoxastic logicCognitionFriendshipDimension (graph theory)

Abstract

fetched live from OpenAlex

When the epistemic chips are down, what ought we believe about our friends? The debate over epistemic partiality bounces between two standard answers: the classic view that we ought believe only for evidential or epistemic reasons, and the partialist view that sometimes we ought to believe better about our friends than the evidence permits. I carve a middle ground, arguing that what we owe our friends is not belief but rather acceptance. I deploy an account on which accepting involves suppressing belief’s characteristic role of guiding cognition, reasoning, and action—a profile I motivate via structural analogy to familiar mechanisms in emotion regulation. The acceptance view highlights novel features of the psychological landscape of partiality cases, capturing the thoroughgoing regulation of diverse cognitive mechanisms, and a diachronic profile of commitment, cognitive effort, and self-control. Moreover, I argue that acceptance avoids pressing objections to the standard views: worries about doxastic control and rationality in friendship (for strong partialist views) and insincerity and the psychological dimension of friendship (for simple evidentialist views). Beyond just intervening in the epistemic partiality debate, a central motivation is to show that this mechanistically precise, psychologically-focused account reveals that acceptance—often dismissed as an unsatisfying cousin to belief—has more resources to make progress on issues in the ethics of belief than is often appreciated.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.671
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.156
GPT teacher head0.371
Teacher spread0.215 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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