A New Model for Industry–Academic Partnerships
Bibliographic Data
| ID | 6096348 |
|---|---|
| Authors | Gary King (0000-0002-5327-7631, Harvard University Press), Nathaniel Persily (0000-0001-9649-3249, Stanford University) |
| Year | 2020 |
| Volume | 53 |
| Issue | 4 |
| Pages | 703-709 |
| Publication date | 2020-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | PS Political Science & Politics (JOURNAL) |
| Journal identifiers | ISSN: 1049-0965 • E-ISSN: 1537-5935 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s1049096519001021 |
| OpenAlex | W2969656656 |
| Language | EN |
| Citations received | 38 |
| References cited | 9 |
The mission of the social sciences is to understand and ameliorate society’s greatest challenges. The data held by private companies, collected for different purposes, hold vast potential to further this mission. Yet, because of consumer privacy, trade secrets, proprietary content, and political sensitivities, these datasets are often inaccessible to scholars. We propose a novel organizational model to address these problems. We also report on the first partnership under this model, to study the incendiary issues surrounding the impact of social media on elections and democracy: Facebook provides (privacy-preserving) data access; eight ideologically and substantively diverse charitable foundations provide initial funding; an organization of academics we created, Social Science One, leads the project; and the Institute for Quantitative Social Science at Harvard and the Social Science Research Council provide logistical help
Big data · Business · Democracy · General partnership · Ideology · Political science · Politics · Public relations · Social media · Sociology · Computer Science · Law · Mobile Crowdsensing and Crowdsourcing · Privacy-Preserving Technologies in Data · Privacy, Security, and Data Protection · Public Administration
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Differentially private survey research
Statistically Valid Inferences from Privacy-Protected Data
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Curation Bubbles
Computational Social Science for Nonprofit Studies
Doing Research - Wissenschaftspraktiken zwischen Positionierung und Suchanfrage
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Toward understanding the impact of artificial intelligence on labor
Misinformation, Disinformation, and Online Propaganda
Social Media, Echo Chambers, and Political Polarization
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Le paradoxe du microciblage au Liban
The effect of academic freedom on electoral democracy in the Asian region
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Advocating for Platform Data Access
Who counts in civically engaged research? Rethinking expertise and authority in politics
Who Is Curating My Political Feed? Characterizing Political Exposure of Registered U.S. Voters on Twitter
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Social media and social impact assessment
Computational Social Science and Sociology
| Unique citing works | 38 |
|---|---|
| Citations per year | 5,43 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 27 |