JOURNAL ARTICLE
"We Became What She Taught Us": A History of Colegio Altamirano, 1897–1958.
Published In: US Latina & Latino Oral History Journal, 2025, v. 9. P. 5 1 of 3
Database: Sociology Source Ultimate 2 of 3
Authored By: Goetz, Philis M. Barragán 3 of 3
Abstract
The Voces Oral History Center at the University of Texas at Austin conducted sixteen interviews with individuals who attended El Colegio Altamirano, the most famous escuelita in Texas. Although scholars have known about El Colegio for several decades, many details about its existence have remained within the community, rather than within an archive, until now. The experiences of these interviewees, who are part of the last group of students to have attended Hebbronville's famous escuelita before it closed in 1958, reveal the impact El Colegio had on their lives, as well as the school's relationship with the community. Beyond what these interviews provide on the micro level, they also make enormous contributions on a larger scale. Focused on the 1930s to 1950s, they are the missing link in piecing together a complete history of El Colegio. Additionally, these interviewees' experiences are connected to the generations of students who came before them. One of the first generations of El Colegio graduates—in many cases the parents or grandparents of the interviewees—established a Hebbronville council for LULAC in 1931. These interviews with El Colegio graduates from one or two generations later demonstrate the pervasiveness of these early activists' values. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:US Latina & Latino Oral History Journal. 2025/01, Vol. 9, p5
- Document Type:Article
- Subject Area:History
- Publication Date:2025
- ISSN:2574-0180
- Accession Number:190956374
- Copyright Statement:Copyright of US Latina & Latino Oral History Journal is the property of University of Texas Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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