Marisa Ponti
Biographic Data
| ID | 3650704 |
|---|---|
| NAME | Marisa Ponti |
| GIVEN NAMES | Marisa |
| FAMILY NAME | Ponti |
| SIGNATURE | PONTI M |
| AFFILIATIONS | University of Gothenburg |
| ORCID | 0000-0003-4708-4048 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 16 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Narratives of epistemic agency in citizen science classification projects
Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the resul…
Human-machine-learning integration and task allocation in citizen science
The field of citizen science involves the participation of citizens across different stages of a scientific project; within this field there is currently a rapid expansion of the integration of humans and AI computational technologies based on machine learning and/or neural networking-based paradigms. The distribution of tasks between citizens (“the crowd”), experts, and this type of technologies has received relatively little attention. To illus…
Collaboration matters
Projects producing citizen-generating data (CGD) to provide evidence and to drive change have increased considerably in the last decade. Many of these initiatives build on multi-actor collaboration and are often supported by non-governmental organisations (NGOs), the public sector, businesses or community-based organisations. The joint efforts of these actors are often necessary to provide the resources and the support that citizens need to produ…
The Science Of Citizen Science
Emerging models of data governance in the age of datafication
The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data …
Distributing attention across multiple social worlds
Emerging models of data governance in the age of datafication
The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data …
Distributing attention across multiple social worlds
Emerging models of data governance in the age of datafication
The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data …
Collaboration matters
Projects producing citizen-generating data (CGD) to provide evidence and to drive change have increased considerably in the last decade. Many of these initiatives build on multi-actor collaboration and are often supported by non-governmental organisations (NGOs), the public sector, businesses or community-based organisations. The joint efforts of these actors are often necessary to provide the resources and the support that citizens need to produ…
The Science Of Citizen Science
Human-machine-learning integration and task allocation in citizen science
The field of citizen science involves the participation of citizens across different stages of a scientific project; within this field there is currently a rapid expansion of the integration of humans and AI computational technologies based on machine learning and/or neural networking-based paradigms. The distribution of tasks between citizens (“the crowd”), experts, and this type of technologies has received relatively little attention. To illus…
Narratives of epistemic agency in citizen science classification projects
Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the resul…
Computer Science (6 works) · Engineering (5 works) · Political science (5 works) · Species Distribution and Climate Change (4 works) · Citizen science (3 works) · Knowledge management (3 works) · Sociology (3 works) · Artificial Intelligence (2 works) · Business (2 works) · Data science (2 works)