Automated Author ProfileAnwar, Shamena
Anwar, Shamena
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 3.7 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We propose a simple model of trooper behavior to design empirical tests for whether troopers of different races are monolithic in their search behavior, and whether they exhibit relative racial prejudice in motor vehicle searches. Our test of relative racial prejudice provides a partial solution to the well-known infra-marginality and omitted-variables problems associated with outcome tests. When applied to a unique dataset from Florida, our tests soundly reject the hypothesis that troopers of different races are monolithic in their search behavior, but the tests fail to reject the hypothesis that troopers of different races do not exhibit relative racial prejudice.
Authors
- Anwar, Shamena ;
- Fang, Hanming
We propose a simple model of trooper behavior to design empirical tests for whether troopers of different races are monolithic in their search behavior, and whether they exhibit relative racial prejudice in motor vehicle searches. Our test of relative racial prejudice provides a partial solution to the well-known infra-marginality and omitted-variables problems associated with outcome tests. When applied to a unique dataset from Florida, our tests soundly reject the hypothesis that troopers of different races are monolithic in their search behavior, but the tests fail to reject the hypothesis that troopers of different races do not exhibit relative racial prejudice.
Authors
- Anwar, Shamena ;
- Fang, Hanming