What are the Social, Physical and Economic Problems of Slums \nand their Expectations from the Urban City?
Bibliographic record
Abstract
The mechanization of agriculture in Turkey as of 1950s brought about \nconsiderable unemployment in the labour force, as a result of which Turkey experienced \nan accelerated immigration movement from rural to urban areas. The inadequate number \nof dwellings and the insufficient income of this section migrating from rural to urban \nareas to acquire a dwelling of their own played a key role in the unplanned urbanization. \nThis research was conducted with the aim of acquainting with prevailing social, \neconomic and physical conditions in slum areas where Ankara experiences substantial \nincrease in squatting. Through interviews carried out with slum dwellers, being \ncharacterized as people living away from urban culture due to the general stereotype, \ntheir expectations from the urban city and approaches to urban transformation was \ninquired. \nIn the scope of this study, slums and slum policy; urban transformation and urban \ntransformation processes in Turkey were primarily explained in a conceptual manner \nand then a fieldwork was performed. As a result of surveys and interviews conducted in \nYalçınkaya Quarter where fieldwork was also realized, social, physical and economic \nproblems of slums as well as their expectations from the urban city were identified. The \nassessments in this respect revealed that shantytowns display a transitional characteristic \nbetween rural and urban areas and they are on the horns of dilemma as they possess \nneither an urban nor a rural lifestyle. \nThe study conducted in Yalçınkaya Quarter revealed the urban poverty once \nagain; however, meeting with people striving to hold on and struggle for life in the \nquarter where a sort of depression area was expected to be found in ethnical aspects is of \nhigh importance in terms of demonstrating the efforts of these people for maintaining \ntheir relations with the city regardless of existing social, physical and economical \nproblems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".