Bibliographic record
Abstract
Community grants are used by a wide variety of government and non-government bodies at different levels across many jurisdictions. They may be particularly prevalent among local and regional governments and in policy sectors where community development is an approach or goal. Yet there has been little rigorous research into this practice. Few formal evaluation studies have been reported. There is no available synthesis of the rationale behind such programs, effective process designs, or their success in achieving intended outcomes. Planners and other professionals who initiate such programs may have little more than intuition to guide them. Thus, the objective of this Chapter is to review the literature on the use of community grants as a tool for urban and regional planning practice. This review is supplemented with evidence from the author’s own experience of these programs within two western Canadian provinces; while these cases are specific to a particular geographic and political context, the findings are likely generalizable to urban governments in (at the least) other Western liberal democratic regimes. The Chapter concludes by drawing on the literature and cases to make suggestions for urban planning professionals about how to effectively use community granting as a community development tool.
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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".