Integration and "Welcome-ability" Indexes: Measures of Community Capacity to Integrate Immigrants
Bibliographic record
Abstract
This paper aims at clarifying the applicability of the theory of micro-macro links to the general concept of “integration” and illustrates two distinct methods of measuring the concept at individual and community levels. In particular, two indexes are developed, the first one called welcome-ability index, to measure the capacities of communities to welcome and integrate newcomers, and the second called integration index, to measure economic, social, and political integration of individuals. The first, a community-level measure, takes into account opportunities and facilities, including employment opportunities, facilities for health care and positive attitudes towards immigrants. The second, an individual-level measure, takes into account the multi-dimensionality of integration, specifically, economic inclusion and parity, social recognition and belonging, political involvement that insures the legitimacy of institutions, and civic participation. The latter could be considered an outcome of the processes measured by the former. The welcome-ability index is illustrated with data gathered for a project that collated baseline information on Ontario communities served by local partnerships specifically tasked with enhancing the capacities of communities to welcome newcomers. These data were gathered from the 2006 Canadian Census, 2008 Canadian Community Health Survey, Ontario 211 (a service provider database), and City Plans and Policies. The integration index is developed with data from the 2008 Canadian General Social Survey on Social Networks. The paper concludes with suggestions for future research directions by extending the theory of macro-micro links involved in studies of integration.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".