38978 research outputs found
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It must be very hard to publish null results
No full textPublication practices in the social sciences act as a filter that favors statistically significant results over null findings. While the problem of selection on significance (SoS) is well-known in theory, it has been difficult to measure its scope empirically, and it has been challenging to determine how selection varies across contexts. In this article, we use large language models to extract granular and validated data on about 100,000 articles published in over 150 political science journals from 2010 to 2024. We show that fewer than 2% of articles that rely on statistical methods report null-only findings in their abstracts, while over 90% of papers highlight significant results. To put these findings in perspective, we develop and calibrate a simple model of publication bias. Across a range of plausible assumptions, we find that statistically significant results are estimated to be one to two orders of magnitude more likely to enter the published record than null results. Leveraging metadata extracted from individual articles, we show that the pattern of strong SoS holds across subfields, journals, methods, and time periods. However, a few factors such as pre-registration and randomized experiments correlate with greater acceptance of null results. We conclude by discussing implications for the field and the potential of our new dataset for investigating other questions about political science
The Effects of Privacy Policy Presentation and Length on Trust in Recommender Systems: An Online Experiment
No full textRecommender systems play a crucial role in e-commerce by simplifying consumer searches, improving decision making, and increasing user satisfaction, ultimately boosting e-vendors’ revenues. Their effectiveness depends on the trust of the users and the availability of data to generate accurate recommendations. Using an online experiment, we examined the effect of privacy policies and the amount of requested information on trust in recommender systems and willingness to share data. The results showed that a long privacy policy reduced trust compared to a short or absent policy. The presentation of a privacy policy and the request for more data decreased participants’ willingness to share data with the system, but a long policy did not further decrease the sharing beyond the effect of a short policy. To maintain trust and encourage data sharing, e-vendors may benefit from offering privacy policies upon request, keeping them concise, and minimizing data requests
Immunization Metrics and Modelling Studies (IMMS)
No full textImmunization Metrics and Modelling Studies (IMMS) is a four-year global initiative funded by the Gates Foundation. Building on more than a decade of research by the Vaccine Confidence Project (VCP) and the London School of Hygiene \& Tropical Medicine (LSHTM), IMMS aims to identify demand- and delivery-side barriers to vaccination at national and sub-national levels, forecast uptake of new and existing vaccines, and quantify the pandemic’s impact on vaccination demand, among other objectives. IMMS is a dedicated unit within the Vaccine Confidence Project, led by Dr Alex de Figueiredo (Principal Investigator and IMMS Lead) and Prof. Heidi Larson (Co-Principal Investigator and VCP Director). Established as a long-term capability within VCP, IMMS is designed to grow beyond the initial funding period and to provide sustained measurement, modelling, and decision support for immunisation programmes in the years ahead.
This project directory contains project information related to forthcoming outputs of two global waves of data collection including a formal Research Analysis Plan detailing all the planned research and a code/data repository
