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Record W4255821334 · doi:10.31219/osf.io/kwx2n

A New Paradigm for Epistemology: From Reliabilism to Abilism

2020· preprint· en· W4255821334 on OpenAlexaff
John Turri

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCounterfactual thinkingEpistemologyNothingCounterfactual conditionalLuckRelation (database)PhilosophyCommonsense knowledgeReliability (semiconductor)Computer scienceBody of knowledge

Abstract

fetched live from OpenAlex

Contemporary philosophers nearly unanimously endorse knowledge reliabilism, the view that knowledge must be reliably produced. Leading reliabilists have suggested that reliabilism draws support from patterns in ordinary judgments and intuitions about knowledge, luck, reliability, and counterfactuals. That is, they have suggested a proto-reliabilist hypothesis about “commonsense” or “folk” epistemology. This paper reports nine experimental studies (N = 1262) that test the proto-reliabilist hypothesis by testing four of its principal implications. The main findings are that (a) commonsense fully embraces the possibility of unreliable knowledge, (b) knowledge judgments are surprisingly insensitive to information about reliability, (c) “anti-luck” intuitions about knowledge have nothing to do with reliability specifically, and (d) reliabilists have mischaracterized the intuitive counterfactual properties of knowledge and their relation to reliability. When combined with the weakness of existing arguments for reliabilism and the recent emergence of well-supported alternative views that predict the widespread existence of unreliable knowledge, the present findings are the final exhibit in a conclusive case for abandoning reliabilism in epistemology. I introduce an alternative theory of knowledge, abilism, which outperforms reliabilism and well explains all the available evidence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.360
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.120
GPT teacher head0.307
Teacher spread0.186 · 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
GenreOther

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

Citations8
Published2020
Admission routes1
Has abstractyes

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