Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. \n\nFindings: 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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it