The evolution of the third sector during the COVID-19 pandemic: Next generation diasporic civic organizations (DCOs) among Bangladeshis in Toronto
Bibliographic record
Abstract
This paper examines the confluence of civic engagement and cyberspace by studying diasporic civic organizations (DCOs) within superdiverse and digitizing contexts. Civic engagement is crucial for DCOs, which often originate in superdiverse locales in migrant-receiving cities like Toronto. The paper explores how studying superdiverse locales provides a framework to move past ethnocentric interpretations of diasporic civic engagement and how digitization affects their organizations. The study focuses on three next-generation Bangladeshi Canadian DCOs through semi-structured interviews, digital archival analysis, and field notes. Findings show that digitization initially posed challenges due to inadequate support and resources during the early stages of the pandemic. However, digitization ultimately provided less resource-intensive interventions for a more dispersed audience. Simultaneously, unequal access to digital tools negatively impacts less-resourced, volunteer-run DCOs and their service recipients. Policymakers and service providers must find ways to support more effective and equitable digitization for DCOs originating in superdiverse locales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".