Lodewijk Gelauff
Postdoctoral Scholar, Communication
Bio
Lodewijk Gelauff is postdoctoral scholar at the Deliberative Democracy Lab in the Center on Democracy, Development and the Rule of Law. He is also a member of the Crowdsourced Democracy Team. He is a project lead of the Self-Moderating Platform for Online Deliberation, an online video chat platform that can scale small-group conversations with a structured agenda, and the Stanford Participatory Budgeting platform. His work focuses on online technologies for societal decision making.
Lodewijk has been an active contributor and volunteer in the Wikipedia/Wikimedia community in various roles including as a founder and core organizer of the photography competition Wiki Loves Monuments, and was named the 2021 Wikimedia Laureate.
Honors & Awards
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Wikimedia Laureate / 20th Year Honouree, Wikimedia Foundation (2021)
Professional Education
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Doctor of Philosophy, Stanford University, MGTSC-PHD (2023)
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Master of Science, Stanford University, MGTSC-MS (2020)
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Master of Science, Leiden University, Chemistry and Science Based Business (2014)
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Bachelor of Science, Leiden University, Molecular Science and Technology (2010)
Projects
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PB Stanford, Stanford University
PB Stanford is a platform that supports local governments and NGO's to set up a budgeting vote in the Participatory Budgeting framework.
Location
Stanford
Collaborators
- Ashish Goel, Professor of Management Science and Engineering and, by courtesy, of Computer Science, Stanford University
- Sukolsak Sakshuwong, School of Engineering
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Stanford Platform for Online Deliberation, Stanford University
An online video platform that helps scale and moderate online deliberations over video chat.
Location
Stanford
Collaborators
- James Fishkin, Stanford University
- Ashish Goel, Professor of Management Science and Engineering and, by courtesy, of Computer Science, Stanford University
- Kamesh Munagala, Professor of Computer Science, Duke University
- Sukolsak Sakshuwong, School of Engineering
- Alice Siu, Communication
Lab Affiliations
All Publications
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Opinion Change or Differential Turnout: Changing Opinions on the Austin Police Department in a Budget Feedback Process
Digital Government: Research and Practice
2024; 5 (3): 1-32
View details for DOI 10.1145/3664822
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Rank, Pack, or Approve: Voting Methods in Participatory Budgeting
AAAI Conference on Web and Social Media
2024
View details for DOI 10.1609/icwsm.v18i1.31326
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Fair and Inclusive Participatory Budgeting: Voter Experience with Cumulative and Quadratic Voting Interfaces
Design for Equality and Justice. INTERACT 2023
2024
View details for DOI 10.1007/978-3-031-61698-3_6
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ACHIEVING PARITY WITH HUMAN MODERATORS A self-moderating platform for online deliberation
ROUTLEDGE HANDBOOK OF COLLECTIVE INTELLIGENCE FOR DEMOCRACY AND GOVERNANCE
2023: 202-221
View details for DOI 10.4324/9781003215929-15
View details for Web of Science ID 001086105000015
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Opinion Change or Differential Turnout: Austin’s Budget Feedback Exercise and the Police Department
EAAMO '22: Equity and Access in Algorithms, Mechanisms, and Optimization
2022
View details for DOI 10.1145/3551624.3555295
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Wiki Loves Monuments: crowdsourcing the collective image of the worldwide built heritage
Journal on Computing and Cultural Heritage
2022
View details for DOI 10.1145/3569092
- Robust Allocations with Diversity Constraints Neural Information Processing Systems (NeurIPS) 2021
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The City of Austin FY 2022 Budget Survey
Stanford Crowdsourced Democracy Team.
Stanford.
2021
Abstract
This budgeting survey was collaboratively designed by the City of Austin Budget Office and the Stanford Crowdsourced Democracy Team. The purpose was to gain constructive insights into the budgeting prefer- ences of residents of Austin as input for the FY2022 budget process. It builds on a similar exercise in the previous Fiscal Year, with the difference that responses in the expenditure section are now only collected categorically, rather than with detailed budget preferences. The survey consisted of three sections: Revenue, Expenditure and Demographics. The survey was completed and submitted by 1237 respondents.
Stanford Digital Repository -
The City of Austin FY 2021 Budget Survey
Stanford Crowdsourced Democracy Team.
2020
Abstract
This budgeting survey was collaboratively designed by the City of Austin Budget Office and the Stanford Crowdsourced Democracy Team. The purpose was to gain constructive insights into the budgeting prefer- ences of residents of Austin as input for the FY2021 budget process. The survey was first published on May 1 with an anticipated end date of May 31, and then extended due to popular demand into June. It was designed before the impact of the COVID-19 crisis became apparent, and the increased attention for the Black Lives Matter movement in May. The survey was completed and submitted by 37,006 respondents. We observe a steep increase of the number of responses on May 31, which coincided with an increased activity in the Black Lives Matter movement after the killing of George Floyd by the hands of police and calls for reducing police funding in general.
Stanford Digital Repository -
The City of Long Beach FY 2021 Budget Exercise - Final Report
Stanford Crowdsourced Democracy Team.
Stanford.
2020
Abstract
This budgeting exercise was designed by the Stanford Crowdsourced Democracy Team. The purpose was to both allow residents of Long Beach to gain insights in the budgeting trade-offs, and let them provide input to the City for their FY2021 budget process. The survey was first published on August 4 and responses were collected until September 4. The survey was designed in coordination with the City of Long Beach budgeting department. The City is facing a budget shortfall of about $ 30 million, and in this exercise the residents are asked to choose between various budgeting policies that would together add up to address that deficit. The survey consisted of two sections: Budget and Demographics. The exercise was presented as a ”glimpse of some of the tough choices managers have to make in balancing the City’s budget”. The exercise did not pretend to represent the City’s proposed budget or adds/cuts that are up for consideration, but rather stand-ins.
Stanford Digital Repository - Who Is in Your Top Three? Optimizing Learning in Elections with Many Candidates AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 2019: 22–31
- Schrijven voor Wikipedia van Duuren Media. 2018
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Comparing voting methods for budget decisions on the ASSU ballot
Stanford University.
Stanford, CA.
2018
Abstract
During the 2018 Associated Students of Stanford University (ASSU; Stanford’s student body) election and annual grants process, the Stanford Crowdsourced Democracy Team (SCDT) ran a research ballot and survey to develop insights into voting behavior on the budget component of the ballot (annual grants) where multiple grant requests (hereafter: ‘projects’) are considered. We provided voters with additional voting methods for the budget component, collected further insights through a survey and demonstrated the viability of the proposed workflow.
- International comparison of technology transfer data University Technology Transfer Routledge. 2016: 428–435
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A molecular cage of nickel(II) and copper(I): a [{Ni(L)(2)}(2)(CuI)(6)] cluster resembling the active site of nickel-containing enzymes
CHEMICAL COMMUNICATIONS
2009: 2700–2702
Abstract
A new mononuclear low-spin nickel(II) dithiolato complex, [NiL(2)] (1), reacts with copper iodide to form the hetero-octanuclear cluster [{Ni(L)(2)}(2)(CuI)(6)] (2) with four trigonal-planar CuI(2)S and two tetrahedral CuI(2)S(2) sites; anagostic interactions between the nickel(II) ions and aromatic protons have been demonstrated by variable-temperature NMR studies to pertain in solution.
View details for DOI 10.1039/b900423h
View details for Web of Science ID 000265890600024
View details for PubMedID 19532926