Bibliographic record
Abstract
The proliferation of social media-based initiatives aimed at asylum seekers and refugees in recent years is evidence of growing interest in the potential of social media for delivering interventions and messages to refugee populations in host countries. However, surprisingly little is currently known about how refugees routinely use and incorporate social media into their everyday lives in host countries, and their motivations for doing so. The aim of the study reported in this paper was to explore how and why young refugees living in Norway use social media in their everyday lives, to identify capabilities associated with this use, and to make connections with well- being. The researchers adopted a qualitative approach, undertaking in-depth interviews with eight young refugees and two key informants involved in running social media sites aimed at refugees. Amartya Sen’s Capability Approach (1987) was used to frame the study and guide the analysis of findings. Findings indicated that participants’ main motivations for using social media were communication, access to information, and learning. Analysis of their reported achievements suggested that social media offered five related capabilities which could have an important role in advancing well-being: effective communication; social connectedness; participation in learning opportunities; access to information; and expression of self. Other findings, such as differences in approach to using social media (‘active’ and ‘passive’ use) are discussed. Although all participants used social media and recognised its importance to their lives, variations in the way they approached and valued it suggest that providers need to consider these factors when using it as a tool to engage refugees.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".