Bibliographic record
Abstract
While self‐help housing is most frequently analyzed in developing countries, this entry describes how it is also widespread in Europe, Canada, and the USA, where, historically, vernacular self‐building was commonplace in rural villages and among frontier homesteaders. In urban areas, too, notable self‐help housing experiences are a feature throughout the twentieth century, while today self‐building and self‐management of dwelling development and expansion continue to be a primary method of housing production in low income peri‐urban neighborhoods in Texas and Southern states. In addition, self‐help improvements and DIY (do‐it‐yourself) are an important element in home improvement and housing refurbishment among middle income populations in contemporary Europe and the USA. Widely analyzed in developing countries, self‐help became a significant driver of low income housing development in informal settlements as an outcome of rapid urbanization in the second half of the twentieth century and the inability of the public and private sectors to meet housing demand. From the late 1970s, public policies became largely supportive of self‐help, installing basic infrastructure in previously unserviced communities and providing households with legal title to their homes.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.068 | 0.015 |
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".