Community Action Planning: Is it the key to unlocking the door to Homelessness?
Bibliographic record
Abstract
describes the new trends that have lead governments at all levels in North America and Europe to attempt to “ involve community ” in both policy development and service delivery decisions. The process of community centred programming must address historical, demographic, community health, social, ethnic and cultural, economic and political factors and is complex enough when it involves municipal governments and geographically bound neighborhoods or relatively homogenous social or cultural communities at the local level. Involving this multidimensional concept of community becomes even more complex in a federal system when national governments attempt to forge collaborative governing arrangements with local communities crossing several layers of jurisdictional boundaries. In spite of these challenges, several significant experiments are underway in Canada to develop this new type of government partnership with communities that illustrates the inherent difficulties as well as the possibilities. One such bold experiment is the Supportive Communities Partnership Initiative (SCPI) part of the National Housing Initiative introduced in 1999 to alleviate homelessness. A cornerstone of SCPI is the establishment of a Community Action Plan in each selected community which may
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".