Integrating regression and multiobjective optimization techniques to analyze scientific perception
Abstract Science holds high prestige in society and understanding public perception of what is considered scientific is essential. The scientificity of a profession is the degree of scientific legitimacy and is determined by the quality of its scientific procedures. Higher levels of scientificity ar...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-89065-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Science holds high prestige in society and understanding public perception of what is considered scientific is essential. The scientificity of a profession is the degree of scientific legitimacy and is determined by the quality of its scientific procedures. Higher levels of scientificity are achieved when scientific results are more objective, impartial, and neutral. In this work, we first estimate the scientificity levels attributed to various professions using a logistic regression model. Then, we explore ways to simultaneously improve their scientific perception by means of multiobjective optimization techniques. To this aim, the statistical results are used to formulate a multiobjective optimization model that maximizes the scientific perception of all the professions considered. The findings provide insights into science policy measures to optimize resource allocation in order to increase the scientific perception of the professions. |
|---|---|
| ISSN: | 2045-2322 |