The Political Inclusion of Migrants in Multi-Ethnic Cities: Toronto Compared
Bibliographic record
Abstract
One of the hallmarks of globalization is increased mobility. While much research has explored the flows of capital, information, and goods to cities, less attention has been paid to the implication of popular mobility for governing the city. Migrants often come with radically differing histories, cultures, skills, priorities and needs. In addition, the conditions under which populations are received (hostile of supportive) offer important insights into migrant political behavior. Do different communities react differently to migrant inclusion policy interventions? Does hyper diversity affect local migrant political action? Does migrant political behavior reflect attachments to place or ethnic identity? This paper takes a preliminary look at ethno-cultural inclusion policies - in Toronto, London, and Amsterdam. All three cities are hyper diverse, and considered to be in the vanguard of inclusion policy efforts. I use the term ‘inclusion’ policy to reflect the idea that policy in this area has been developed and targeted to a variety of outcomes – political, social and economic. This paper will compare migrant inclusion policies, and will begin to analyze the relationship between policies, demographic contexts and political and economic behaviors. As a first cut, this paper presents findings from our correlation analyses of political inclusion (through municipal – voter registration and voter turnout), economic inclusion (through analysis of unemployment, income and access to public benefits). In Amsterdam census and electoral data was drawn from 2002 and 2006 at the City District level from the City of Amsterdam, for Toronto data was drawn from ward profiles for 2001 and 2006. The election coordinator for the City of Toronto provided data on municipal elections in Toronto. London election data was provided by Colin Rallings and Michael Thrasher (University of Plymouth). Data for the London case was also derived from Census, and reports from local and national government offices. Institutional benchmarking data was derived from the Migration Integration Policy Index. In what follows we offer first a brief description of the demographic context of the cities being compared, this is followed by a discussion of policy and program development in each case. We then draw present a brief comparison of our three cases, and end with a preliminary consideration of the implications of these institutional differences for migrant integration, and what lessons it might offer for subsequent research on how we study migration, integration policies and the city.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".