Bibliographic record
Abstract
Throughout the world a greater proportion of the population are living in cities which are growing because of in-migration. Many accounts of the migrations and of migrants in cities have been written. While most accounts emphasize the alienation and disorganization of the migrant, there are a growing number of accounts which indicate that the migrant helps and is helped by his kin group. These latter accounts have been analyzed in an attempt to discover the significance of kinship in rural-urban migration. The literature relating to migration theory has been briefly reviewed. The theory of William Petersen was found most useful but the typology he proposed is too general to contribute much understanding to the problem of rural-urban migration. The two types of Petersen's theory into which the rural-urban migration fit have been expanded into four types or levels of rural-urban migration. Each of the four types is characterized by different control of land resources, participation in ceremonial life and recognition of kinship rights and obligations. These are assumed to be interdependent. Case studies are used to illustrate types. These cases confirm that while there is a considerable lessening in the range of economic obligations to kin, the size of the potential kin group does not shrink. While the potential kin circle is large, the member of the kin group in the city selects, on the basis of personal preference, those whom he considers effective kin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".