Writing mobility: thoughts from a research project on British working holiday makers in Australia
Bibliographic record
Abstract
I remember a young woman with an Irish accent and a Thai sarong draped over her shoulders. She was talking to a man with a Canadian flag on his backpack. They were competing with travel stories, and sharing pub-quiz knowledge of Australia and other places. In the background, a couple of young men wearing Premiership football shirts sang the Dutch national anthem. And a Japanese woman passed by dragging a bright pink suitcase on wheels behind her. I remember conversations about the future. Someone listed the places they wanted to visit before they died. Someone else detailed their own plans, which involved getting married, buying a house, having kids, and growing old in the same place they grew up in, surrounded by family and old friends. I remember a young man with a neuropathic bladder. He was reluctant to leave the country in which his condition was diagnosed. He worried that he might not get the catheters he needed as easily elsewhere. I remember news of a detention centre in the middle of the desert, where refugees from other countries were held behind razor wire for years on end.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".