Reimagining Communities through Transnational Bengali Decolonial Discourse with YouTube Content Creators
Bibliographic record
Abstract
Colonialism--the policies and practices wherein a foreign body imposes its ways of life on local communities--has historically impacted how collectives perceive themselves in relation to others. One way colonialism has impacted how people see themselves is through nationalism, where nationalism is often understood through shared language, culture, religion, and geopolitical borders. The way colonialism has shaped people's experiences with nationalism has shaped historical conflicts between members of different nation-states for a long time. While recent social computing research has studied how colonially marginalized people can engage in discourse to decolonize or re-imagine and reclaim themselves and their communities on their own terms--what is less understood is how technology can better support decolonial discourses in an effort to re-imagine nationalism. To understand this phenomenon, this research draws on a semi-structured interview study with YouTubers who make videos about culturally Bengali people whose lives were upended as a product of colonization and are now dispersed across Bangladesh, India, and Pakistan. This research seeks to understand people's motivations and strategies for engaging in video-mediated decolonial discourse in transnational contexts. We discuss how our work demonstrates the potential of the sociomateriality of decolonial discourse online and extends an invitation to foreground complexities of nationalism in social computing research.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".