Unpacking the micro-macro nexus: narratives of suffering and hope among refugees from Burma recently settled in Australia
Bibliographic record
Abstract
Narratives of forced migration are open to a variety of interpretations. In mental health, refugee narratives of arduous journeys in the face of systemic macro socio-political forces are often transformed from this context into a medicalized micro context of inner individual worlds. Both the dominant pathogenic lens of trauma studies and the growing salutogenic lens embodied in resilience research, often reflect a western cultural idiom of focusing on the individualized nature of these phenomena. Using qualitative data collected from refugees from Burma now settling in Australia, the article emphasizes the need for a more reflexive and expansive account of both suffering and hope within refugee narratives. It recounts these narratives within a conceptual framework which acknowledges the importance of the connections between the micro individual experience and the macro, socio-political context. This is not only a question of political principle, but also a matter of listening to the voice of those who know most about the relationship between macro forces of human rights violations and their impact on individual, family and community trajectories.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.029 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".