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Probabilistic Acceptance

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Henry E. Kyburg Jr

Artificial Intelligence · Submitted 6 Feb 2013

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show that the difficulties of probabilistic acceptance are not as severe as they are sometimes painted, and that though there are oddities associated with probabilistic acceptance they are in some instances less awkward than the difficulties associated with other nonmonotonic formalisms.

how it is stated · asserted 1.00 read the record →

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arXiv ID1302.1556 (version 1)
Pith ID55O55CUE, Pith's permanent record of this paper and its review
CitationHenry E. Kyburg Jr. "Probabilistic Acceptance." arXiv:1302.1556 (2013). https://pith.science/paper/55O55CUE
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,555 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.76 · human participants 0.03 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.02 · compares with prior results 0.81 · mentions code or data availability 0.01.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The idea of fully accepting statements when the evidence has rendered them probable enough faces a number of difficulties. We leave the interpretation of probability largely open, but attempt to suggest a contextual approach to full belief. We show that the difficulties of probabilistic acceptance are not as severe as they are sometimes painted, and that though there are oddities associated with probabilistic acceptance they are in some instances less awkward than the difficulties associated with other nonmonotonic formalisms. We show that the structure at which we arrive provides a natural home for statistical inference.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The idea of fully accepting statements when the evidence has rendered them probable enough faces a number of difficulties. background 1.00 0.09 0.05 asserted 1.00
s1 We leave the interpretation of probability largely open, but attempt to suggest a contextual approach to full belief. contribution 0.72 0.85 0.08 hedged possibility 0.76
s2 We show that the difficulties of probabilistic acceptance are not as severe as they are sometimes painted, and that though there are oddities associated with probabilistic acceptance they are in some instances less awkward than the difficulties associated with other nonmonotonic formalisms. result 0.90 0.91 0.64 asserted 1.00
s3 We show that the structure at which we arrive provides a natural home for statistical inference. result 0.52 0.88 0.18 asserted 1.00

What kind of paper the classifier reports this is: position or commentary 0.86 · evidence from argument 0.99.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The idea of fully accepting statements when the evidence has rendered them probable enough faces a number of difficulties. We leave the interpretation of probability largely open, but attempt to suggest a contextual approach to full belief. We show that the difficulties of probabilistic acceptance are not as severe as they are sometimes painted, and that though there are oddities associated with probabilistic acceptance they are in some instances less awkward than the difficulties associated with other nonmonotonic formalisms. We show that the structure at which we arrive provides a natural home for statistical inference.

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