Bibliographic record
Abstract
The Global Credit Crises that began like a small fire in the US housing finance market in 2007 spread and became a forest fire that first engulfed the US, then the Western economies, and eventually the rest of the world, including India. The crisis is clearly the deepest and the most widespread economic meltdown that the world has faced since the Great Depression. Indian industry started experiencing the real impact of the global financial meltdown from the last quarter of 2008. The Indian economy, which was on a robust growth path up to 2007-08, averaging at 8.9 per cent during the period 2003-04 to 2007-08, witnessed moderation in 2008-09, with the deceleration turning out to be somewhat sharper in the third quarter. IT industries, financial sectors, real estate owners, car industry, investment banking and other industries as well are confronting heavy loss due to the fall down of global economy.The Real Estate Business has seen 62 per cent decline in revenues, 58 per cent decline in PBDIT, and 78 per cent decline in net profit, between March 2008 and March 2009. This decline has been accompanied by a significant fall in the property prices in India.The importance of the real estate sector in India cannot be understated given the strong forward and backward linkages that it generates. The sector has demand implications for intermediate inputs like steel, cement, etc., while keeping afloat the whole construction industry including transport and other intermediate labour services. Given its importance for the economy it is worthwhile to see how adverse expectations are playing a role in this sector and what the possible solutions are.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".