Assessing the impact of community building efforts on the social networks of inner city residents
Bibliographic record
Abstract
When I heard that Winnipeg Habitat for Humanity was attempting to initiate a project targeting a neighbourhood in need of help - William Whyte - I leapt at the opportunity to get involved and offered my services as a researcher. My first observations in this Winnipeg neighbourhood included several homes in disrepair (peeling paint, rotting wood, broken windows, etc.), litter on the streets including shopping carts, graffiti, empty lots and condemned notices on boarded-up houses. I knew the area had problems. Located on the northern edge of Winnipeg's inner city, the William Whyte neighbourhood has been marked by a steadily decreasing population, economic decline, physical deterioration and a relatively high crime rate over the last thirty to forty years. As a result, the area has experienced a large portion of its existing housing stock being converted to rental units, a significant number of vacant lots and several abandoned or condemned houses dotting its streets. These are symptoms of an important issue facing Winnipeg at the turn of the millennium: urban decay. An emigration of population, arson and other criminal activities, an aging housing stock, among other factors, have all contributed to the current state of affairs present in Winnipeg's inner city residential neighbourhoods. As residents who can afford to, move away and sell their homes, often of older stock, the population for an area declines (Leo, Shaw et al. 1998). The remaining residents tend to be people who cannot afford to leave. Fewer people and a smaller tax base leads to aging infrastructure and decreased municipal services...
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".