Connecting immigrant communities to local government : the case of Richmond, BC
Bibliographic record
Abstract
Richmond, British Columbia stands out amongst Canadian cities by having one of the nationally highest concentrations of immigrants in its population, as 62% of Richmond’s residents are first generation immigrants. Although immigrants from China and Hong Kong constitute a significant percentage of overall immigrants to Richmond, in the last 8 years, there have been notable numbers immigrating from the Philippines, Taiwan, and India. Along with the high population of immigrants comes the challenge of engaging newcomers as well as non-English speakers in unfamiliar City government processes. In response to the identified needs and recommended actions from the 2013- 2022 Social Development Strategy as well as the 2012-2015 Richmond Intercultural Strategic Plan, the Connecting Immigrant Communities to Local Government project was created. This project attempts to answer: How can the City of Richmond support immigrant civic engagement? Civic engagement is defined as knowledge about civic processes and the capacity to participate in local planning and governance. The project reached out to three different groups of stakeholders to receive input about opportunities to improve immigrant civic engagement. These three groups included City of Richmond staff, staff from immigrant-serving community organizations, and immigrant residents in Richmond. In Richmond, the non-profit and public sector offer various programs that focus on sharing information with newcomers about how City Hall and other levels of government function. There are also a few initiatives that try to strengthen connections between immigrants and City Hall. The gap that exists in Richmond is a long-term educational program that provides consistent support and training for immigrants on how to engage with their City and community at a decision-making level. Immigrant residents face multiple barriers when interacting with City Hall. The primary barrier is language, as many newcomers have limited English language skills and City staff may be using high-level, professional means of communication. The Canadian government’s federal structure can be confusing for both newcomers and established immigrants, and the City’s consultation processes can also be intimidating for people who do not regularly interact with City Hall. Immigrants from certain countries may have a strong mistrust of government due to their former experiences with more authoritative governance systems. For some immigrants, City Hall may not be a welcoming space due to a perceived lack of staff and citizen participants from their racial background. City staff also face various challenges when they are attempting to engage immigrant residents in their planning processes. Staff struggle with providing appropriate translation due to a lack of resources and clear corporate guidelines. Staff also face difficulties finding translators trained in the City’s technical fields. City staff often conduct outreach to the general public as a whole and are limited in their capacity to target specific demographics due to time and financial constraints. Moreover, engaging immigrant residents in meaningful discussions requires facilitation skills that many staff are not trained in. The City of Richmond has a role in supporting immigrant civic engagement and there are opportunities for the City to improve their outreach to immigrant communities. The following table is a summary of this report’s recommendations, based on the feedback from this project’s participants.
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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 | 0.001 | 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 | 0.043 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".