Resilient Hope: Identity, Extensibility, and Freedom as Design and Policy Considerations in Social Computing
Bibliographic record
Abstract
This article contributes to the social computing (particularly CSCW and HCI) scholarship by conceptualizing the idea of “hope”, particularly in relation to displacement, marginalization and resistance. Based on our longitudinal and multi-phase ethnographic interventions with internally displaced populations (IDPs) in Mohakhali and Kalyanpur areas of Dhaka, Bangladesh for three consecutive years, this work demonstrates how various dynamic properties of hope, both at personal and communal level, support this group to act, react, and/or resist many layers of urban adversities in their quotidian lives. By introducing the notion “Resilient Hope” , which constitutes identity formation, radical extensibility, and freedom of departure, the paper offers a novel understanding of how such marginalized communities sustain an uncertain, yet hopeful life. Drawing from a rich body of literature in Anthropology, STS, Philosophy, and Critical Urban Studies, it argues that resilient hope for a marginal community is not a static end goal to be achieved through design but a dynamic mode of survival and operation that has the potential to inform sustainable CSCW and HCI design processes. The paper further connects its empirical findings and theoretical insights to the broader goal of social justice in computing.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.063 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".