This item is accessible only to the Washington University community.
Off-Campus WUSTL Users: Click the “Off-Campus Download” button below. You will be prompted to log in using your WUSTL Key.
Language
English (en)
Date of Award
3-1-2016
Author's Department
International and Area Studies
Degree Name
Bachelor of Arts (A.B.)
Restricted/Unrestricted
Restricted
Abstract
The Republic of Turkey is an unambiguously ethnically pluralistic nation. The people of Anatolia are as diverse as they are resilient to political shift—a point of regional pride, which has often fueled powerful pre-national sociopolitical identitiesgrounded in territorial integrity and expectant of a certain degree of autonomy in development. The collapse of the Ottoman Empire and its system of semi-autonomous vilayets ushered in a new era in Anatolia, one no longer governed by recognition of pluralism and mutual-benefit. Instead, the Republican Era has aimed to rewrite history, assimilate, and homogenize the populations with the borders of Turkey to create a new national identity rooted first and foremost in citizenship. Despite a concentrated effort of the Republic to deteriorate or subordinate ethnic allegiances in favor of propagating the consciousness of the nation-state, these subnational ethnic identities remain divisive both in conviction and regional localization of distinct populations. This study seeks to recognize the diversity of historic and modern Turkey and where possible circumnavigate the Republican idea of national identity for the purpose of understanding development at the regional level as it relates to ethnic territory.
DOI
https://doi.org/10.7936/y2bc-f653
Mentor
Sukkoo Kim
Additional Advisors
Fatemeh Keshavarz
Recommended Citation
Pulley, Cameron, "Gelişme Kimin İçin? (Development for Whom?): Provincial Growth and Ethnic Bias in Republican Turkey, 1927-2014" (2016). Senior Honors Papers / Undergraduate Theses. 180.
The definitive version is available at https://doi.org/10.7936/y2bc-f653
Comments
Accessibility Notice: This document is a scanned text that has been automatically processed using Python's Tesseract Optical Character Recognition (OCR) engine to provide a text alternative for assistive software. Formatting or character recognition discrepancies may exist.