Towards a Researcher–Advocacy Model for Asylum Seekers: A Pilot Study Amongst East Timorese Living in Australia
Bibliographic record
Abstract
During the 24-year Indonesian occupation of East Timor, widespread human rights abuses led to the flight of political dissidents to neighboring countries. We report a pilot study assessing a ‘Researcher– Advocacy’ model among East Timorese asylum seekers residing in Australia. The aim was to combine elements of advocacy, quantitative and qualitative research, and strategic assistance in a program of engagement with this marginalized group. Thirty-three consecutive asylum-seeker clients attending a newly formed clinic participated in the study, representing a quarter of the known population of asylum seekers from East Timor living in Sydney at the time. High levels of trauma including torture and other human rights abuses were recorded. Respondents also reported a wide range of resettlement and adaptational difficulties, particularly relating to their uncertain residency status. Eighty percent met criteria for one or more psychiatric disorder. The wider benefits of the study included the extension of services to a group that previously had shown a reluctance to seek assistance for traumatic stress, the engagement of the exile community as a whole, and building the capacity to respond both in Australia and in East Timor to the humanitarian emergency of 1999. Scientific limitations of the model included the labor-intensive nature of the program, the small and selective sample recruited and incomplete data collection.
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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 | 0.027 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".