Return migration, a case study from Swan River Valley, Manitoba
Bibliographic record
Abstract
Migration is a fact of life experienced by many human beings. A large body of academic studies have been devoted to migration, but a significant aspect of migration has been largely ignored by most social sciences, or explained away as anomalies or with simple causal models, usually economically based. Return migration represents a significant amount of the total migration flow around the world. This thesis is comprised of a case study of return migrants from the Swan River Valley area of the Province of Manitoba, Canada, as well as an examination and comparison between various other case studies of return migration from around the world. Beginning from a basis of rejecting singular causal factors, especially those of an economic nature, this study is an attempt to show, in a holistic manner, the causes and effects of return migration. Some of the most significant factors causing migrants to return to their region of origin include the desire to live close to family, familiarity with local networks in the region of origin and a rejection of many aspects of urban living. Although more involved research needs to be done, the findings of this research shows that understanding the factors affecting return migration could have strong implications for academic studies of migration as well as for government policy makers in the areas of migration and immigration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".