International mobility and transnational media use : Evidence from East York
Bibliographic record
Abstract
Drawing on the fourth cycle of East York study interviews with 101 Torontonians, we investigate the ways in which they stay connected within and across borders. Previous research has ample evidence that international migrants have regular interpersonal contact with their family and friends who live in different nation-states, thanks to advances in telecommunication technologies. Contrary to earlier work that studied specific migrant groups from one country of origin at a time, the unique dataset of East York study enables us to study a variety of persons with different mobility histories to Toronto. We compare international migrants to those who have migrated from elsewhere in Canada, those who have moved within the greater Toronto area, and those who have stayed in place. In so doing, we have a broad, and yet, fine-grained understanding of how different mobile persons’ practice transnationality, meaning the degree of having and communicating with personal ties across borders. We ask to what extent the respondents kept in touch across borders and distances, which communication channels they used, and whether the type of tie and the respondents’ region of origin affect the maintenance of that particular tie. Contributing to communication, migration, and network studies, this paper advances knowledge by its in-depth analysis of both mobile and non-mobile Torontonians’ mode of communication, depending on the type and location of their personal ties.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".