Technology mediated care as infrastructure: the role of social media in supporting international migrants settling in New Zealand small towns
Bibliographic record
Abstract
Considerable research has focused on the important role of social media in migrant life. However, there is a lack of knowledge about how remote regional areas shape migrants’ use of social media, and whether social media can serve as a viable migrant support infrastructure in such places. We seek to help address this knowledge gap by investigating how social media platforms have enabled algorithmic care for migrants to Oamaru, a small service town on the south-east coast of the South Island of New Zealand. We develop an analytic of technology-mediated care as infrastructure in a tripartite relationship between people, place and platform to guide our examination of the small town migrant support ecosystem. A questionnaire survey of migrants and a community Facebook page have been the source of our data. We observe the important role played by social media platforms through the availability of emotional, informational and material support, and also how, over time, the community Facebook page has evolved into a self-organising and generative care infrastructure. The findings confirm that this form of platformised care is not placeless, but rather contingent on place-specific relations and responsibilities by bringing together migrants, host communities and small town institutions. Social media facilitates the practice of both self-care and caring-with others, enabling migrant and host community interactions and cultural competency building, as well as addressing pre-existing migrant support deficiencies in small towns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".