The Perspectives of Commercial Property Stakeholders in Post-Disaster Rebuild
Bibliographic record
Abstract
This paper seeks to expand our theoretical knowledge on what happens in post-disaster rebuild from the perspective of commercial property stakeholders (investors and developers; agents and professionals). By exploring how they have fared in a post-disaster rebuild environment, this paper has provided a nuanced understanding of the complex challenges they face after a major disaster. This paper adopts an interpretive approach to understanding what it takes to rebuild in a post-disaster environment through the lens and experiences of property stakeholders. After a series of catastrophic earthquakes in 2010 and 2011 in Christchurch, New Zealand, there was consequential damage to most of the commercial buildings in the central business district (CBD). Seven (7) years on, the CBD is still being rebuilt after so much demolition and clearing of debris for an extended period when the city was cordoned off just after the earthquakes. For this study, qualitative data was gathered via semi-structured interviews from twenty (20) purposively identified “Informed Property Stakeholders” involved in post-disaster rebuilding. The interview findings were subjected to an interpretative and thematic analysis used to provide a veracious way of characterising the viewpoints of those interviewed. Overall, this paper has highlighted the perspectives of those interviewed and the findings conveyed that post-disaster rebuilding was an unprecedented and unusual challenge for many property stakeholders.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".