Review on Repercussions of Covid-19 Pandemic on Construction Sector
Bibliographic record
Abstract
The Covid-19 pandemic has disrupted almost everything related to economic and livelihood activities. This review paper however aims at showing and assessing the negative impacts of Covid-19 over construction sector in India particularly. The construction sector in India being second highest employee intensive sector after agriculture. It had come to an abrupt halt in first quarter of FY2020-21 and this resulted in stopping of supply chain management, major infrastructural projects of government and private entities, different cement, steel and other construction material manufacturing units. Further it resulted in sudden unemployment over millions of Indian employees and workers, in both organized and unorganized sectors related to construction businesses. The lockdown gave rise to many unfair practices in the business of supply chain, one of which is the stockpiling of cement and steel in the initial period of strict lockdown, hence the prices soared up in short time. That’s why estimates of major projects also rose up. On the other hand, the rates of real estates have gone down invariably due to cash crunch in the economy. All these effects and causes are dealt with in this research paper using authentic data in the public domain
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".