The Impact of Labor Migration on African Families in South Africa: Yesterday and Today
Bibliographic record
Abstract
Kinship systems and the various types of social support they offer in reducing the short-term costs of settlement in the country of destination have generated a great deal of interest during the last two decades. This study examines the kinship networks of recent Mexican immigrant women to the eastern end of the Texas/Mexico (The Gulf Coast) border. For purposes of the present study kinship networks are conceptualized as linking, otherwise, individual economic actors seeking social mobility in the United States, to stable social structures alone, both sides of the U.S./Mexico border. Attention is given to the resources that flow across linkages and their consequences for the success or failure of the migration process. Participant observation and in-depth interviews are used in studying network ties of recent Mexican immigrant women. Forty-four complete dyads consisting of twenty-two immigrant women and at least one kin participated in the study during a two-year period. Whenever possible additional kin members were also interviewed. Findings reported here indicate that binational networks operate on both sides of the border offering protection to newcomers, mainly undocumented workers, and facilitating their incorporation into the informal economic sector of the border economy. Moreover, for many immigrant women both sending and receiving networks were found within the country of origin. Reasons other than economic need were found to be important to the migration process. Results point to the socioeconomic diversity of the Mexican women who are migrating to the U.S. Mexico border area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".