Bibliographic record
Abstract
The City of Toronto has become synonymous with themes of culture and diversity. With close to one half of the city’s population now comprised of those born outside of Canada, Toronto represents a dynamic and exciting cultural mosaic. Yet, underneath this surface exist real disparities in health and well being for many newcomers and racialized communities. In addition to and because of such disparities, changes in the demographics of Toronto have led to challenges and questions involving the participation of such communities within the formal political realm. Much research to date has focused on issues of representation and the exercise of political franchise within such communities. As opposed to the formal political realm, the aim of this research is to better understand the substantive participation of newcomers and members of racialized communities in processes of government sponsored citizen participation at the municipal level by asking: what is the ability of this approach to policy making to meaningfully include a diverse range of voices? This question is important because it is decisions in this realm that most immediately impact residents of the city. Additionally, if social policy developers are to keep up with shifts in demographics and create inclusive and responsive policy, then consideration must be given to all community members. To understand the ability of government sponsored citizen participation to be inclusive of a diverse range of voices, a literature review was conducted. Also, an analysis of the case of the 2000-2001 Community Consultations on Social Development held in Toronto was undertaken. Finally, five interviews were held with policy practitioners within Toronto to gain insight into the ability of practices of government sponsored citizen participation to be inclusive. The findings of this research study highlight that political will and increased funding must be directed towards the purposeful inclusion of newcomers and racialized communities in processes of government sponsored citizen participation so as to foster increased experiences of inclusion and the creation of responsive and effective policies of social development.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.034 | 0.030 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".