Bibliographic record
Abstract
Context: While there is emerging evidence of large spikes in housing stress, high unemployment and mental health issues across Australian households, very little is known about the unique experiences of members of share houses. Members of share houses are more likely to be young; in casual employment; at risk of homelessness; in informal, short-term and over-crowded living situations; and born overseas than the general population. These factors represent overlapping layers of vulnerability during a pandemic and require devoted research and policy attention. The data reported in this paper is based on 1052 responses to an online survey released between June 9 and June 20 2020. The survey was targeted at anyone who had lived in a share house in Victoria in 2020. Findings: The survey found that 74% of respondents had lost their job or had their hours reduced, 47% had seen their income reduced, 50% reported that their mental health had deteriorated since the beginning of COVID-19, 39% had changed their housing arrangement, 22% could not pay their mortgage or rent on time in the last 3 months and 20% had gone without meals to afford other expenses. Significantly, 44% of respondents were in housing stress and almost a quarter reported feeling stressed by how crowded their home is. 40% of respondents attempted to renegotiate their rent and 50% were successful.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".