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On Modeling the Costs of Censorship

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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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Michael Carl Tschantz, Sadia Afroz, Vern Paxson, J. D. Tygar

Cryptography and Security · Submitted 10 Sep 2014

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

The authors' main claim We argue that the evaluation of censorship evasion tools should depend upon economic models of censorship.

how it is stated · asserted 0.92 read the record →

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arXiv ID1409.3211 (version 1)
Pith IDS66XYNV2, Pith's permanent record of this paper and its review
CitationJ. D. Tygar, Michael Carl Tschantz, Sadia Afroz, Vern Paxson. "On Modeling the Costs of Censorship." arXiv:1409.3211 (2014). https://pith.science/paper/S66XYNV2
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, 4,020 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.09 · human participants 0.05 · controlled comparison 0.23 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.90 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

We argue that the evaluation of censorship evasion tools should depend upon economic models of censorship. We illustrate our position with a simple model of the costs of censorship. We show how this model makes suggestions for how to evade censorship. In particular, from it, we develop evaluation criteria. We examine how our criteria compare to the traditional methods of evaluation employed in prior works.

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 We argue that the evaluation of censorship evasion tools should depend upon economic models of censorship. contribution 0.80 0.25 0.60 asserted 0.92
s1 We illustrate our position with a simple model of the costs of censorship. method 0.80 0.92 0.06 asserted 0.97
s2 We show how this model makes suggestions for how to evade censorship. contribution 0.47 0.93 0.13 asserted 0.91
s3 In particular, from it, we develop evaluation criteria. contribution 0.88 0.93 0.13 asserted 0.99
s4 We examine how our criteria compare to the traditional methods of evaluation employed in prior works. contribution 0.77 0.88 0.09 asserted 0.74

What kind of paper the classifier reports this is: position or commentary 0.74 · evidence from argument 0.84.

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

We argue that the evaluation of censorship evasion tools should depend upon economic models of censorship. We illustrate our position with a simple model of the costs of censorship. We show how this model makes suggestions for how to evade censorship. In particular, from it, we develop evaluation criteria. We examine how our criteria compare to the traditional methods of evaluation employed in prior works.

Human review

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How the literature uses it

1 arXiv paper cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 1 sentence without a confident label

Cites it as background 1 paper

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