An Exploratory Method for an Alternative Narrative of Housing History in Istanbul
Bibliographic record
Abstract
Analysis and visualization of spatial data has proven to be an effective tool in communicating apparent or latent spatial information in an efficient way. Here a specific data processing and visualization method is employed in creating an original base for a nonlinear historical narrative of a specific urban phenomenon. Housing development in Istanbul is selected as a case to be explored through a heuristic method involving correspondence analysis (COA) complemented by clustering methods and Bertin's graphics theory. COA is an exploratory method for cross-tabular data analysis, which facilitates the interpretation of data by visualizing the relative relationship between its variables. Two sets of data on the shares of the state, the private sector, and housing cooperatives in housing development in Istanbul between 1987 and 2007 have been processed by COA. The outputs of the process are correspondence maps and Bertin graphics. Maps create a basis for a spatiotemporal analyses of public and private housing development in Istanbul, while revealing certain relation networks and breaks in housing history that are not quite perceptible by looking at the data tables only. Thus, drawing upon the discoveries from the maps, a nonlinear narrative of housing history is proposed as a collection of several thorough analyses of the discoveries within the economic, political, and social setting particular to this geography.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".