Social media narratives, diasporic identity and collective memory: A critical synthesis of the literature
Bibliographic record
Abstract
This study investigates the evolving relationship between social media narratives, diasporic identity, and collective memory in a context marked by increasing migration and growing digital media engagement. Employing a scoping review as a meta-analysis approach, we analyzed scholarly literature from 2014 to 2024 across Diaspora Studies, Sociology, and Communication Studies to understand how cultural and identity narratives are evolving amid fast-developing digital technologies. Out of 250 sources collected, 69 were shortlisted for in-depth review based on their relevance to the research questions. The study reveals a dichotomy in digital narratives concerning diasporic identity and collective memory, highlighting both positive potentials and negative drawbacks. On the positive side, digital narratives can foster empowerment, memory preservation, and community building. On the negative side, they may pose challenges to personal and collective identity, exhibit anti-democratic tendencies, and undermine cultural diversity. The research concludes by proposing a new analytical framework for examining diasporic identity and collective memory in relation to social media narratives, along with specific suggestions for future research in this ever-evolving ecosystem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.007 |
| 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".