Mapping the Social Justice Ecosystem: An Emerging Typology

Background and Purpose:

Persistent inequalities in income, housing, food access, health, and racial justice continue to shape local realities across the United States. Community-based organizations (CBOs) respond through diverse strategies and theories of change, yet existing frameworks inadequately capture their structural roles within broader social justice ecosystems—limiting researchers’ and practitioners’ ability to assess how local ecosystems build capacity for collective, democratic action. This study addresses that gap by developing and applying two complementary analytic tools: the Action Research Mapping Agent (ARMA), a structured analytic framework operationalized through AI-assisted coding, and the Social Justice Ecosystem Typology (SJET), an inductively derived classification of organizational function.

The guiding research questions are: (1) How can a social justice ecosystem be systematically mapped to assess its capacity for collective action? and (2) What organizational roles emerge within a local urban context, and how do they contribute to ecosystem-level capacity for structural change?

Methods:
This study employed a constructivist qualitative design using inductive thematic analysis and iterative typological development. The analytic sample consisted of 20 purposively selected CBOs representing diverse roles within the Silver Spring/Montgomery County, Maryland social justice ecosystem (two organizations were excluded due to insufficient publicly available data). ARMA functioned as a structured analytic framework to systematically code organizational characteristics across seven dimensions: theory of change, power-building, policy and advocacy, root-cause framing, governance and accountability, partnerships, and funding base. Coding was based on publicly accessible data (websites, annual reports, IRS Form 990s), with source citations and confidence ratings recorded to support auditability. Human–AI Collaborative Inquiry (H-ACI) extended established practices of reflexive qualitative analysis, using structured researcher–AI dialogue as a form of iterative analytic memoing, peer debriefing, and triangulation. Analytic rigor was enhanced through an interpretive audit trail, explicit coding criteria, systematic reconciliation of AI-assisted and researcher-generated interpretations, and negative case analysis of organizations that resisted typological classification.

Results:
Analysis generated a five-part typology of organizational functions: Accompanier (relational and immediate support; n=10), Promoter (advancing community priorities in policy and advocacy spaces; n=4), Architect (building relational and institutional infrastructure; n=2), Alternative (creating community-controlled systems outside dominant institutions; n=2), and Illuminator (cultivating critical consciousness and collective imagination; n=1). A sixth meta-category, Movement Organization, describes entities operating fluidly across multiple roles (n=1). Several organizations initially assigned mid-range ARMA scores were reclassified through typological analysis, demonstrating that linear scoring alone can obscure deeper ecosystem significance.

Conclusions and Implications:
Findings suggest that the Silver Spring ecosystem is heavily weighted toward service and accompaniment, with comparatively few actors driving structural transformation—indicating potential fragility in community-level capacity for collective democratic action. SJET and H-ACI offer a replicable framework for ecosystem-level structural analysis that expands methodological approaches in social work research. Preliminary application with local stakeholders suggests utility in identifying gaps in power-building infrastructure and informing cross-organizational strategy. This work contributes to advancing social work research that supports democratic capacity-building and systemic change.