JOURNAL ARTICLE
Challenging Deficit Discourses with Counterstories of Cultural Wealth: Multilingual Learners' Pursuit of Career and Technical Education.
Published In: Teachers College Record, 2025, v. 127, n. 11/12. P. 60 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Emerick, Mark R. 3 of 3
Abstract
This article examines how deficit narratives about multilingual learners of English (MLs) influence their enrollment and experiences in career and technical education (CTE) programs, using an ethnographic study at a large regional CTE center in the northeastern United States. It finds that teachers and administrators often reproduce deficit discourses portraying MLs as lacking agency, familial support, and motivation, while MLs themselves share counterstories grounded in community cultural wealth (CCW)—including aspirational, familial, social, and navigational capital—that highlight their purposeful engagement with CTE as a pathway to career and life goals. The study reveals a disconnect between educators' deficit-based stock narratives and MLs' asset-based testimonios, emphasizing the need for CTE leaders and teachers to challenge deficit thinking and recognize MLs' cultural wealth to foster equitable access and participation. It also discusses the complexities of MLs navigating neoliberal educational contexts and calls for professional development and systemic reforms that center MLs' experiences and counterstories to disrupt inequities in CTE access and outcomes.
Additional Information
- Source:Teachers College Record. 2025/12, Vol. 127, Issue 11/12, p60
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:0161-4681
- DOI:10.1177/01614681251414207
- Accession Number:190929334
- Copyright Statement:Copyright of Teachers College Record is the property of Sage Publications Inc. 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.