Good 'grassroots' government, a millennium model for Winnipeg
Bibliographic record
Abstract
Local government is often cited as the 'level of government closest to the people'. This reference comes from the recognition that the functions provided by local government, for the most part, have an impact on the every day lives of citizens. Given the fiscal constraint being experienced in all levels of government today, and the need to rethink what, how, and if services are to be maintained, it is logical to assume that citizens would be interested in the decision-making processes that are occurring. Yet, the level of political literacy appears to be on the decline, and citizen apathy towards local government continues. Indeed, this apathy is often attributed to a lack of citizen knowledge and awareness of the issues, which concern local government. In its final form, this apathy translates into an attitude of mistrust and dissatisfaction with how our communities are governed. This thesis presents an opportunity to examine the concept of public participation, as well as the historic and current mechanisms for citizen involvement in local government. It explores the concept of the 'neighbourhood' as an organizational framework for citizen engagement and decision-making in local governance structures. This thesis develops an innovative neighbourhood model for local government that seeks to re-engage the citizen; to provide authority and decision-making power at the level of the neighbourhood; to strengthen community capacity; and to renew citizen faith and trust in government.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".