Bibliographic record
Abstract
Although by no means an exhaustive survey of current knowledge about and understandings of homelessness as it occurs in rural areas of the ‘developed’ world, the preceding chapters suggest thematic discourses which offer vital clues to the next phase of research into rural homelessness. It is immediately obvious from these accounts that the previously reported propensity to regard homelessness as an urban phenomenon is by no means restricted to the UK and the US where the bulk of research on this issue has so far been carried out. Throughout the book, authors refer to the widespread perception that homelessness is an urban problem, and that where it occurs in rural areas it is unseen, unacknowledged and largely unattended. The overlooking and underestimation of rural homelessness is linked to a broad assumption that if rural people become homeless they will gravitate to the cities where infrastructure exists to meet their needs. Homelessness, then, becomes conflated with out-migration. Whether because a nation as a whole carries the reputation of being ‘well-housed’ (as is reported here in the cases of Canada and New Zealand), or whether rural areas themselves are seen as housing-rich, or spaces of community and well-being, the very idea of there being homelessness in rural areas can turn into the literally unthinkable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".