Bibliographic record
Abstract
This book presents the results of a four-year academic-community partnership, "Integration Trajectories of Immigrant Families," funded by a Partnership Development Grant from the Social Sciences and Humanities Research Council of Canada.The academic partners consist of a team of faculty members at Ryerson University, who are affiliated with the Ryerson Centre for Immigration and Settlement.This research centre was the lead institution of the project.The initial community partners were COSTI, the Ontario Council of Agencies Serving Immigrants, METRAC, the Scarborough Housing Help Centre, Social Planning Toronto, the Toronto Workforce Innovation Group, the Wellesley Institute, and WoodGreen.The project also benefitted from close links with the master's program in Immigration and Settlement Studies and the PhD in Policy Studies program at Ryerson University.Numerous graduate students participated in the research, dissemination activities, and project administration.The project consisted of several phases.In the initial phase, the partners reviewed the academic literature and community practices related to the role of families in the migration and settlement process.In the second phase, the partners conducted a qualitative study, including interviews with migrant families.The third phase, of which this book is a key part, involved knowledge dissemination and mobilization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.270 | 0.077 |
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".