Sustainability Trends in Humanitarian Architecture Research: A Bibliometric Analysis
Bibliographic record
Abstract
Despite the increasing need for Post-Disaster and Post-Conflict (PDPC) sheltering, and the rising number of humanitarian architects, there is a vague understanding of how “sustainable” shelters in PDPC situations are being addressed in the literature. Therefore, this paper aims at mapping and analyzing the current status and development trends in research that associates sustainability and shelters in PDPC situations during the past four decades (1982–2022) using a bibliometric analysis. This was fulfilled using VOSviewer to identify and visualize literature development trends, active journals, productive authors, contributing countries, influential institutions, and keyword networks. The findings identified four phases of the development process: no recognition (1982–2002), initiation (2003–2012), rapid growth (2013–2017), and accelerated growth (2018–2022). In terms of publications, the “International Journal of Disaster Risk Reduction” and “Sustainability” are the key journals publishing in the field, whereas Gibson and Habert are the most publishing authors. The United States of America was found to be the leading country in the research field, albeit Université de Montréal in Canada was the most active in terms of institutions. The study suggests the promotion of social and economic standards in addition to the environmental while developing sustainable shelter solutions. It also advises shelter professionals from both public and private sectors to improve their collaborations with all related 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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.211 | 0.263 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".