MULTI'CRITERIA EXPERT BASED ANALYSIS FOR RANKING THE URBAN GENTRIFICATION DRIVERS IN DEVELOPING COUNTIRES
Bibliographic record
Abstract
More than 40 years have passed since the term “gentrification” was coined by Ruth Glass (Torrens & Nara, 2007). Originating from Britain, gentrification has become popular concept in developed countries and much research has been conducted in the US, Europe, Canada and Australia since the 1970s on gentrification of the inner cities (Bounds & Mourris, 2008; Hamnett, 1991). Research was also conducted in some premier cities of developing world such as Mexico, Istanbul, Ankara and Seoul (Ha, 2004; Ergun, 2004; Guzey, 2006; Jones & Varley, 1999). Most of the gentrification researchers come to the point that appearance of the already formulated origins of gentrification are time and place-specific (Guzey, 2006), as this urban phenomenon through an evolutionary process found different aspects and drivers. The reason is that through its evolution from late 1950s different preconditions have brought different logics and outcomes in different geographies. Thus it is worth to threat gentrification as a complex phenomenon by sophisticated tools to examine the ideas and hypotheses behind it. In this research it is intended to use the analytical network process (ANP) integrated with GIS to figure out the gentrification drivers in Kuala Lumpur inner city and rank them according to their influence. This would provide a decision support system as tool par excellence for exploring the expert idea based on time and place. Besides, the methodology will foster the future works on modeling and simulating the behaviors of gentrification in developing countries that have not been applied hitherto.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".