Bibliographic record
Abstract
Economic recovery refers to the process by which businesses and local economies return to conditions of stability following a disaster. Its importance and complexity are being increasingly recognized in disaster risk reduction research and practice. This paper provides an overview of current research on economic recovery and suggests a research agenda to address key gaps in knowledge. Empirical studies have provided a number of robust findings on the disaster recovery of businesses and local economies, with particular insights into short- and long-term recovery patterns, influential factors in recovery, and disparities in recovery across types of businesses and economies. Modeling studies have undertaken formal analyses of economic impacts of disasters in which recovery is usually addressed through the incorporation of resilience actions and investments in repair and reconstruction. Core variables for assessing and understanding economic recovery are identified from the literature, and approaches for measuring or estimating them are discussed. The paper concludes with important gaps in the development of a robust theory of economic recovery. Systematic data collection is needed to establish patterns and variations on how well and how quickly local economies recover from disasters. Research is urgently needed on the effectiveness of resilience approaches, decisions, and policies for recovery at both the business and local economy levels. Detailed, testable theoretical frameworks will be important for advancing understanding and developing sound recovery plans and policies. It will be especially important to consider the relationship between economic recovery and recovery of the built environment and sociopolitical fabric of communities in developing a comprehensive theory of disaster recovery.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".