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Scholars have long recognized that teachers’ social interactions play an important role in their learning and professional development. Still, while a growing body of research shows that teaching-focused social ties can give precollege educators access to valuable information, knowledge, and advice—or "social capital"—that improves professional practice and student learning, empirical, mixed methods studies on the phenomenon in the higher education sector are rare, and few investigate what conditions are necessary for these social ties to develop among college instructors. Focusing on college faculty in 17 associate- and baccalaureate-level institutions in one U.S. city, this study uses survey and interview data to explore the connections between structural and positional educator characteristics and the "social networks," or compilations of social ties, in which faculty reported discussing teaching. Regression analyses of survey responses (n = 244) indicate that fewer years of teaching experience, the time faculty take preparing to teach, discipline, and institution type are correlated with social network dimensions linked to improved professional practice. An inductive analysis of interview data from a subset of faculty (n = 22) supplements survey findings with descriptions of how teaching experience, organizational support, and other factors constrain and reinforce the development of teaching-focused social ties. Results confirm and extend prior research indicating that the development of teaching-focused social networks and the accrual of ties linked to social capital demand faculty and organizational investment. Findings also suggest that leaders hoping to foster beneficial ties should tailor instructional initiatives to more closely align with faculty experience and time commitments.
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Keeping policy commitments: An organizational capability approach to local green housing equity
Abstract Affordable housing that incorporates sustainability goals into its design has the potential to address both health and economic disparities via enhanced energy‐efficiency, structural durability and indoor environmental quality. Despite the potential for these win‐win advances, survey data of U.S. local governments indicate these types of equity investments remain rare. This study explores barriers and pathways to distributional equity via energy‐efficient housing. Using archival city sustainability survey data collected during a period of heightened U.S. federal investment in local government energy‐efficiency programs, we combine machine learning (ML) and process‐tracing approaches for modeling the complex drivers and barriers underlying these decisions. First, we ask, how do characteristics of a city's organizational learning methods—its administrative structure, past experience with housing programs, resources, stakeholder engagement and planning—predict policy commitments to green affordable housing? Using ensemble ML methods, we find that three specific modes of organizational learning—past experience with affordable housing programs, seeking assistance from neighborhood groups and the technical expertise of professional green organizations—are the most impactful features in determining city commitments to constructing green affordable housing. Our second stage uses process‐tracing within a specific case identified by the ML models to determine the ordering of these factors and to provide more nuance on green‐housing policy implementation.
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- Award ID(s):
- 1941561
- PAR ID:
- 10486870
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Review of Policy Research
- Volume:
- 41
- Issue:
- 1
- ISSN:
- 1541-132X
- Format(s):
- Medium: X Size: p. 135-159
- Size(s):
- p. 135-159
- Sponsoring Org:
- National Science Foundation
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