Research inquiries for digital diaspora and digital diasporic media
Bibliographic record
Abstract
Diaspora and diasporic media have been studied consistently. As research subjects, however, their transition to, or creation on, digital platforms makes it increasingly ambiguous to distinguish digital diaspora from digital diasporic media. This study examines literature on digital diaspora and digital diasporic media and explores this ambiguity by looking at how these two research subjects have been conceptualized and methodologically approached. Referring to Candidatu, Leurs and Ponzanesi’s typology of digital diaspora research, this study concludes that the ambiguity is associated with shifting research paradigms in both internet and digital diaspora studies. The study further offers new insights that digital diaspora and digital diasporic media diverge on the projects each pursues and the nature of content each delivers, but converge on ‘media’ in the age of social media and the increasingly transcultural initiatives of these media. The study also offers the areas of further research that emerge from this ambiguity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".