Empowering Marginalized Communities: A Framework for Social Inclusion
Bibliographic record
Abstract
Social inclusion—the ability to participate fully in one’s social world—is gaining importance in policy and academic circles. Information systems research has shown how addressing digital divides and expanding individual capabilities could increase the inclusion of marginalized groups. Yet while these contributions are notable, much of early research often overlooked the deep-seated power relations embedded in social structures—organized patterns of relationships, norms, and institutions that perpetuate inequalities and hierarchies based on gender, race, ethnicity, and caste. However, the field has evolved to bring a more nuanced understanding of how social inclusion can be achieved during the implementation of digital projects. Building on these emerging insights, in this paper, we explore how a social infomediary—an intermediary addressing social issues through information provision to marginalized communities—uses a digitally enabled agriculture extension project to build social inclusion in communities. Drawing on a qualitative case study of a social intermediary in India, our research highlights the role of social context in facilitating and constraining social inclusion efforts. Based on our findings, we develop a 4R social inclusion framework for digital development projects that shows the importance of recognition, reposition, representation, and reciprocation in fostering social inclusion. We also identify corresponding processes: transformative narratives and dialogues, empathic scaffolding, structured discursive spaces, and innovative interdependence. We discuss the practical and theoretical implications of our research and provide future research directions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.053 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".