| NSF Org: |
SES Division of Social and Economic Sciences |
| Recipient: |
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| Initial Amendment Date: | September 19, 2016 |
| Latest Amendment Date: | October 18, 2021 |
| Award Number: | 1626775 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Brian Humes
SES Division of Social and Economic Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
| Start Date: | October 1, 2016 |
| End Date: | July 31, 2022 (Estimated) |
| Total Intended Award Amount: | $190,137.00 |
| Total Awarded Amount to Date: | $211,422.00 |
| Funds Obligated to Date: |
FY 2017 = $93,581.00 FY 2018 = $21,285.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
660 S MILL AVENUE STE 204 TEMPE AZ US 85281-3670 (480)965-5479 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
P.O. Box 876011 Tempe AZ US 85287-6011 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Political Science, LSS-Law And Social Sciences |
| Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT 01001819DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
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General Summary
Why do we observe different levels of respect for human rights in different regions of the same country? Furthermore, why are citizens? human rights generally uniformly protected (or abused) within the borders of some countries while within other countries these rights are generally upheld in some locations and severely restricted in others? Prior research investigating patterns of human rights protection and violation has typically treated states as centralized decision-makers and examined state respect for human rights as a single, countrywide phenomenon. This approach masks important variations in the actors perpetrating abuses, motives for the abuse, targets of the abuse, and severity of abuse. The PIs propose that cross-national human rights researchers must break their focus on the country as the unit of analysis and look at the sub-national characteristics of repressive behaviors. The PIs focus on three major factors: 1) antigovernment activity, 2) government decentralization, and 3) local government capacity. They argue that antigovernment dissent encourages government agents to respond with high levels of repression. However, this response is particularly likely when government power is highly decentralized, when the dissent takes place far from the national capital, and when the local government is largely incapable of controlling its repressive agents. The PIs collect the first dataset to document the level of repression at the subnational level for a global sample of countries. These data are likely to be used by government agencies, international organizations, non-governmental organizations, and others to engage in evidence-based policy and advocacy.
Technical Summary
While levels of state repression and the frequency, severity, and targets of human rights abuses vary spatially within states, most previous studies of these topics have only considered repression in the aggregate. This is problematic because it ignores variation in institutional structures and decision-making processes within countries. The PIs explain this subnational variation of repression within states. In particular, they focus on three major factors: antigovernment activity, government decentralization, and local state capacity. They develop a global dataset that captures violations of physical integrity rights by state agents at the level of the sub-national unit. For this project, the PIs rely on a mix of expert coding, theoretically informed measurement models, and computational techniques, which are capable of coding and then linking together the diverse information drawn from a set of primary source documents. Using this information, they generate standards-based measures for each of several specific types of physical integrity violations (arbitrary detention, torture, disappearances, and extrajudicial execution) as well as a combined indicator for these abuses for each first-order subnational administrative unit within a state. This level of analysis brings scholarship closer to the level at which most citizens encounter the government's legal, political, and bureaucratic authority.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
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Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This research project is part of a collaborative National Science Foundation grant entitled The Sub-National Analysis of Repression Project (SNARP). SNARP is a theoretically motivated data collection, categorization, and measurement project, that represents an ongoing collaboration with Rebecca Cordell (University of Texas at Dallas), Chad Clay (University of Georgia), Reed Wood (University of Essex), Thorin Wright (Arizona State University) and Christopher J. Fariss (University of Michigan). The ultimate goal is a dataset containing estimates of human rights respect and abuse at the sub-national level of a global sample of countries. The project first identified each individual allegation of human rights abuse contained within a set of human rights documents. For each allegation, of which there are hundreds of thousands, the project team used hand coding, dictionary-based approaches, and supervised machine learning methods to categorize information about the perpetrator of the human rights abuse, the victim of the abuse, and information about the spatial and temporal context of the abuse. In some cases, these allegations are quite specific. In other cases, these allegations are rather general. To combine this information into comparable subnational and country- year estimates, we expand on several measurement models for repression using the allegation data classified in this project. To date, the SNARP collaborative research project has generated a large-scale, publicly available dataset including 163,512 unique human rights abuse allegations in 196 countries between 1999 and 2016 (https://dataverse.harvard.edu/dataverse/SNARP).
Two peer-reviewed articles have been published, one in the Journal of Human Rights explores how US State Department Reports have changed over time as a function of presidential transitions, and another in International Studies Quarterly that outlines the method and approach used to generate the above-mentioned dataset of allegations. Other papers are also in development.
Overall, SNARP sets a new standard for transparency and accessibility of human rights reporting content. It also will serve as the foundation for any new human rights coding projects that use the country year human rights reports as part of the document corpus because it is now the most easily accessible and searchable database for content from three annual human reports: the State Department of the United States, Amnesty International, and Human Rights Watch. SNARP continues in several directions (1) new dictionary development, (2) updated new sets of sentences and allegations (pre-1998 and 2017-2020), (3) new machine learning classification modeling and validation of all categorized allegation features (e.g., scope, intensity, actor, location), (4) validation of subnational coding (capital city, region, geo-coding, location dictionaries), (5) validation of actor coding (UCDP, GED actor dictionaries), (6) SNARP web app and near-real time human coding, (7) allegation and sentence forecasting from new online information sources (e.g, social media data). All of this material is being added to the publicly available dataverse repository for the benefit of the human rights research community.
Last Modified: 11/29/2022
Modified by: Thorin Wright
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