Bibliographic record
Abstract
Acknowledgments ix Editor s Note xi 1 The Financial Crisis and HousingMarkets Worldwide: Similarities, Differences, and Comparisons 1 Ashok Bardhan, Robert H. Edelstein, and Cynthia A. Kroll PART I The United States Leads the Housing Bubble s Rise and Collapse 2 The U.S. HousingMarket and the Financial Crisis 23 Ashok Bardhan, Robert H. Edelstein, and Cynthia A. Kroll PART II The European Union One Continent, Many Markets: A Gauge of Government Institutions and Interventions 3 The 2008 Financial Crisis and the Danish Mortgage Market 53 Jacob Gyntelberg, Kristian Kjeldsen, Morten Baekmand Nielsen, and Mattias Persson 4 Prolonged Crisis 69 Thies Lindenthal and Piet Eichholtz 5 The Dynamics of the Irish HousingMarket 101 Simon Stevenson 6 House Prices and Market Institutions: The Dutch Experience 135 Dirk Brounen and Piet Eichholtz 7 Real Estate Boom and Crisis in Spain 157 Antoni Sureda-Gomila 8 The UK and Europe s Selective Housing Bubble 173 Christine Whitehead and Kathleen Scanlon PART III Eastern Europe: European Emerging Markets Ride the Waves 9 The HousingMarket in Russia: Lessons of the Mortgage Crisis 195 Nadezhda Kosareva and Andrey Tumanov 10 The HousingMarket and Housing Finance in Russia and Its Regions: A Quantitative Analysis 229 Carsten Sprenger and Branko Uro sevi'c 11 The HousingMarket in Serbia in the Past Decade 247 Dejan S os kic', Branko Uros evic', Bos ko Z ivkovic', and Milos Boz ovic' PART IV Asia Housing Bubbles Past, Present, and Future: Contrasts among Asian Economic Giants 12 Irrational Prosperity, HousingMarket, and Financial Crisis: An Empirical Study of Beijing 271 Lu Ping, Zhen Hui, and Xu Yuehong 13 HomeMortgage and Real Estate Market in Shanghai 295 Jie Chen 14 Evolution of the Indian Housing Finance System and HousingMarket 319 R. V. Verma 15 The HousingMarket and Housing Finance under Liberalization in India 343 C. P. Chandrasekhar 16 The Recent Financial Crisis and the HousingMarket in Japan 357 Miki Seko, Kazuto Sumita, and Michio Naoi PART V Managing Housing Bubbles and Housing Markets in Diverse Asian Economies 17 Comparing Two Financial Crises: The Case of Hong Kong Real Estate Markets 377 Charles Ka Yui Leung and Edward Chi Ho Tang 18 The Global Financial Crisis and the Korean Housing Sector: How Is This Time Different from the 1997 Asian Financial Crisis? 399 Kyung-Hwan Kim 19 Government Policy, Housing Finance, and Housing Production in Singapore 421 Lum Sau Kim 20 Taiwan: Housing Bubbles and Affordability 447 Chin-Oh Chang and Ming-Chi Chen PART VI Avoiding Contagion in Other Markets 21 Australia s Economic Response to the Global Financial Crisis and Its HousingMarkets 467 Dogan Tirtiroglu 22 The Financial Crisis and Brazil s Expanding HousingMarket 491 Emilio Haddad and Joao Meyer 23 The Canadian HousingMarket: No Bubble? No Meltdown? 511 Tom Carter 24 Partly Cloudy to Clear: The Israeli Economy and the Local HousingMarket under the Storm of the World Financial Crisis 535 Danny Ben-Shahar and Jacob Warszawski Index 557
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.179 | 0.033 |
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".