Class, Family, Income and Wealth: Farming and Non-Farming Landowners in the Occupational and Social Class Orders in Turkey
Bibliographic record
Abstract
This study presents the trajectory of changes in land ownership and land use and of the differences observed since the mid-1990s in the average amount of annual disposable income (and of wealth) within and between farming and non-farming landowning households in Turkey. The study makes use of the data sets of the Household Budget Surveys conducted by the Turkish Institute of Statistics (TUIK) in 1994, 2002, 2005, 2010, 2015 and 2017. The data sets have been analysed in connection with four main themes: (i) the patterns of structural change in landownership and land use, (ii) the patterns of structural change in the locations of farming and non-farming landowners in the occupational and social class orders, (iii) the patterns of changes and the persistence of differences in the average amounts of annual disposable incomes and wealth within and between the social classes of farming and non-farming landowners and (iv) the effect of family type on the differences of income and wealth. The results indicate that Turkish agrarian structures have undergone significant structural changes in the last quarter of a century, and there are persisting and significant differences of income (and of wealth) at the national level as well as among farming and non-farming landowning households. However, the same kind of differences do not hold true for differences in the average amount of farm land owned. On the contrary, these differences have strong associations with family type among farming as well as non-farming households.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".