Transnational Migration and Digital Memorialization
Bibliographic record
Abstract
As digital outlets of expression become increasingly accessible, means of conveying grief and commemorating the deceased have migrated online. Online memorial websites such as UK-based Muchloved.com boasts thousands of Tributes created by the bereaved to remember the deceased. Many of these Tributes sketch out a rough picture of the person commemorated through text detailing their personal lives, professions, hobbies, and accomplishments, as well as photographs capturing intimate moments with family and community, and condolences contributed by family, friends, and community members. This article examines how stories of migration figure in this large pool of digital Tributes. We draw from Moncur and Kirk’s “emergent framework” for the study of digital memorials by analyzing 17 Tributes on MuchLoved.com, which commemorated individuals who, according to these Tributes, migrated from one nation to another. We find that the practices and conventions of memorial-writing to commemorate first-generation immigrants perpetuate narratives of exceptionality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".