Paper Citation Record · LEDGER

A Tunable Incentive Mechanism for Binary Aggregation Without Verification

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2606.30974.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.30974 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-01T00:45:27.093492Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:33:17.356381Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy24
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation db421ebf-52b6-4ae1-b981-1227c2314369 · outbound

This paper cites Animplementationoffakenewspre- vention by blockchain and entropy-based incentive mechanism.Social Network Analysis and Mining, 12(1):114, 2022.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Animplementationoffakenewspre- vention by blockchain and entropy-based incentive mechanism.Social Network Analysis and Mining, 12(1):114, 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.562796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c987dd7c-8e79-430a-b498-93669b5cc673 · outbound

This paper cites Max- imum likelihood estimation of observer error-rates using the em algorithm.Journal of the Royal Sta- tistical Society: Series C (Applied Statistics), 28(1): 20–28, 1979.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Max- imum likelihood estimation of observer error-rates using the em algorithm.Journal of the Royal Sta- tistical Society: Series C (Applied Statistics), 28(1): 20–28, 1979

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.558869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9be97ea4-cc6f-4a2c-8ca9-2678df9ba37e · outbound

This paper cites Springer Nature.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Springer Nature

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.536595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd7de8de-5dce-4d9b-847b-6c2c87ee9119 · outbound

This paper cites Crowdsourcing with heterogeneous workers in social networks.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Crowdsourcing with heterogeneous workers in social networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.538373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 83f2f418-762d-4c6e-8bd9-3bc3d10b046e · outbound

This paper cites Using truth detection to incentivize workers in mobile crowdsourcing.IEEE transactions on mobile computing, 21(6):2257–2270, 2020.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Using truth detection to incentivize workers in mobile crowdsourcing.IEEE transactions on mobile computing, 21(6):2257–2270, 2020

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.564459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1fc19901-cfac-46e1-9f66-a3255099d8a1 · outbound

This paper cites Online crowd learning with heteroge- neous workers via majority voting.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Online crowd learning with heteroge- neous workers via majority voting

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.571246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ae4e5c6c-ef3f-4c20-a08c-ef42e597a5de · outbound

This paper cites Strategic information revelation in crowdsourcing systems without verification.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Strategic information revelation in crowdsourcing systems without verification

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.552437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dd3a725c-d505-4bb3-8812-6485f1f95939 · outbound

This paper cites A technical survey on statistical modelling and design methods for crowdsourcing quality control.Artificial Intelligence, 287:103351, 2020.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification A technical survey on statistical modelling and design methods for crowdsourcing quality control.Artificial Intelligence, 287:103351, 2020

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.554290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 83c78db9-2889-4229-bff7-05558d5f132f · outbound

This paper cites Bayesian classifier combination.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Bayesian classifier combination

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.576644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8e45e6d5-f1fe-45fa-bd39-631573065df2 · outbound

This paper cites An infor- mation theoretic framework for designing informa- tion elicitation mechanisms that reward truth-telling.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification An infor- mation theoretic framework for designing informa- tion elicitation mechanisms that reward truth-telling

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.578391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aea95102-3dcd-4467-8c45-821992641af8 · outbound

This paper cites Surrogate scoring rules.ACM Transactions on Economics and Computation, 10(3):1–36, 2023.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Surrogate scoring rules.ACM Transactions on Economics and Computation, 10(3):1–36, 2023

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.574947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 604a45bf-cae3-425a-a9ac-2f692a3be233 · outbound

This paper cites Majority rules: how good are we at aggregating convergent opinions? Evolutionary Human Sciences, 1:e6, 2019.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Majority rules: how good are we at aggregating convergent opinions? Evolutionary Human Sciences, 1:e6, 2019

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.534769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 83bfe70b-13ee-4874-95c5-6d1773dbf38b · outbound

This paper cites Eliciting informative feedback: The peer-prediction method.Management Science, 51(9):1359–1373, 2005.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Eliciting informative feedback: The peer-prediction method.Management Science, 51(9):1359–1373, 2005

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.547394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 358a3d80-88dc-45a3-b817-e87ddc5493ba · outbound

This paper cites A bayesian truth serum for subjective data.science, 306(5695):462–466, 2004.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification A bayesian truth serum for subjective data.science, 306(5695):462–466, 2004

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.549097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4bd74db0-1f5b-4eb1-bbf5-a2a96d498ab9 · outbound

This paper cites A robust bayesian truth serum for non-binary signals.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification A robust bayesian truth serum for non-binary signals

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.550782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a1a2bfc3-f40e-421a-9f64-874f0637397c · outbound

This paper cites Learning from crowds.Journal of machine learning research, 11(4), 2010.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Learning from crowds.Journal of machine learning research, 11(4), 2010

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.556520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f45b37b2-8e23-4dd6-9f6c-7b77e7083306 · outbound

This paper cites Two strongly truthful mechanisms for three heterogeneous agents answering one question.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Two strongly truthful mechanisms for three heterogeneous agents answering one question

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.566135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 260e8ebf-7179-4568-a0ae-5e942b3cd048 · outbound

This paper cites Informed truthfulness in multi- task peer prediction.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Informed truthfulness in multi- task peer prediction

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.543788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 32641409-446a-4005-be2c-cbc69d82f63a · outbound

This paper cites Community-based bayesian aggregation models for crowdsourcing.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Community-based bayesian aggregation models for crowdsourcing

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.568014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d205dd8-8cfc-4fb7-a7a0-9e654258e0ec · outbound

This paper cites Labeling images with a computer game.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Labeling images with a computer game

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.580329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aeff567d-6535-487f-af8d-f65038e0764e · outbound

This paper cites Output agree- ment mechanisms and common knowledge.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Output agree- ment mechanisms and common knowledge

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.582034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e089f060-5404-4831-bdf7-f1339753432a · outbound

This paper cites Whose vote should count more: Optimal integration of labels from labelers of unknown expertise.Advances in neural information processing systems, 22, 2009.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Whose vote should count more: Optimal integration of labels from labelers of unknown expertise.Advances in neural information processing systems, 22, 2009

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.569648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df970ff0-b9da-432c-aba3-4ca4422bad5f · outbound

This paper cites Arobustbayesian truth serum for small populations.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Arobustbayesian truth serum for small populations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.545442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8a827bfd-925e-44bb-88ee-b5895fbde9c3 · outbound

This paper cites Reward or penalty: Aligningincentivesofstakeholdersincrowd- sourcing.IEEE Transactions on Mobile Computing, 18(4):974–985, 2018.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Reward or penalty: Aligningincentivesofstakeholdersincrowd- sourcing.IEEE Transactions on Mobile Computing, 18(4):974–985, 2018

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T08:13:30.540307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d2eb14db-f011-48da-be48-5efad97f8d98 · outbound

This paper cites Learning from the wisdom of crowds by mini- max entropy.Advances in neural information pro- cessing systems, 25, 2012.

A Tunable Incentive Mechanism for Binary Aggregation Without Verification Learning from the wisdom of crowds by mini- max entropy.Advances in neural information pro- cessing systems, 25, 2012

Reference 25

Resolution: malformed identifier; raw_fallback, observed 2026-07-07T08:13:30.542042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 14bfbf6f-bce9-417b-8d84-2a644ddf81e9 · inbound

Beyond Byzantine: An Organizational Consensus Algorithm for Self-Interested Agents Under Information Asymmetry cites this paper.

Beyond Byzantine: An Organizational Consensus Algorithm for Self-Interested Agents Under Information Asymmetry A Tunable Incentive Mechanism for Binary Aggregation Without Verification

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T16:33:17.356381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.