Katherine Hoffmann Pham
Biographic Data
| ID | 1246235 |
|---|---|
| NAME | Katherine Hoffmann Pham |
| GIVEN NAMES | Katherine Hoffmann |
| FAMILY NAME | Pham |
| SIGNATURE | PHAM K H |
| AFFILIATIONS | United Nations |
| ORCID | 0000-0002-1022-6025 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 9 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Survey sampling in the Global South using Facebook advertisements
Survey research in the Global South has traditionally required large budgets and lengthy fieldwork. The expansion of digital connectivity presents an opportunity for researchers to engage global subject pools and study settings where in-person contact is challenging. This paper evaluates Facebook advertisements as a tool to recruit diverse survey samples in the Global South. Using Facebook’s advertising platform, we quota-sample respondents in Me…
Survey Sampling in the Global South Using Facebook Advertisements – Corrigendum
Predictive modelling of movements of refugees and internally displaced people
Predicting forced displacement is an important undertaking of many humanitarian aid agencies, which must anticipate flows in advance in order to provide vulnerable refugees and Internally Displaced Persons (IDPs) with shelter, food, and medical care. While there is a growing interest in using machine learning to better anticipate future arrivals, there is little standardised knowledge on how to predict refugee and IDP flows in practice. Researche…
Epidemiological modelling in refugee and internally displaced people settlements
The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several chal…
From Artificial Intelligence Bias to Inequality in the Time of Covid-19
As secretary general of the United Nations, Antonio Guterres said during the 2020 Nelson Mandela Annual Lecture, “COVID-19 has been likened to an X-ray, revealing fractures in the fragile skeleton of the societies we have built.” Without a doubt, the COVID-19 pandemic has exposed and exacerbated existing global inequalities. Whether at the local, national, or international scale, the gap between the privileged and the vulnerable is growing wider,…
Survey sampling in the Global South using Facebook advertisements
Survey research in the Global South has traditionally required large budgets and lengthy fieldwork. The expansion of digital connectivity presents an opportunity for researchers to engage global subject pools and study settings where in-person contact is challenging. This paper evaluates Facebook advertisements as a tool to recruit diverse survey samples in the Global South. Using Facebook’s advertising platform, we quota-sample respondents in Me…
Predictive modelling of movements of refugees and internally displaced people
Predicting forced displacement is an important undertaking of many humanitarian aid agencies, which must anticipate flows in advance in order to provide vulnerable refugees and Internally Displaced Persons (IDPs) with shelter, food, and medical care. While there is a growing interest in using machine learning to better anticipate future arrivals, there is little standardised knowledge on how to predict refugee and IDP flows in practice. Researche…
From Artificial Intelligence Bias to Inequality in the Time of Covid-19
As secretary general of the United Nations, Antonio Guterres said during the 2020 Nelson Mandela Annual Lecture, “COVID-19 has been likened to an X-ray, revealing fractures in the fragile skeleton of the societies we have built.” Without a doubt, the COVID-19 pandemic has exposed and exacerbated existing global inequalities. Whether at the local, national, or international scale, the gap between the privileged and the vulnerable is growing wider,…
Epidemiological modelling in refugee and internally displaced people settlements
The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several chal…
Predictive modelling of movements of refugees and internally displaced people
Predicting forced displacement is an important undertaking of many humanitarian aid agencies, which must anticipate flows in advance in order to provide vulnerable refugees and Internally Displaced Persons (IDPs) with shelter, food, and medical care. While there is a growing interest in using machine learning to better anticipate future arrivals, there is little standardised knowledge on how to predict refugee and IDP flows in practice. Researche…
Survey sampling in the Global South using Facebook advertisements
Survey research in the Global South has traditionally required large budgets and lengthy fieldwork. The expansion of digital connectivity presents an opportunity for researchers to engage global subject pools and study settings where in-person contact is challenging. This paper evaluates Facebook advertisements as a tool to recruit diverse survey samples in the Global South. Using Facebook’s advertising platform, we quota-sample respondents in Me…
Survey Sampling in the Global South Using Facebook Advertisements – Corrigendum
Political science (3 works) · Business (2 works) · Computer Science (2 works) · COVID-19 epidemiological studies (2 works) · Disease (2 works) · Displaced person (2 works) · Engineering (2 works) · Forced migration (2 works) · Management science (2 works) · Medicine (2 works)