Advanced documentation technologies for a people-centred preparedness and re-construction in Bela, India
Bibliographic record
Abstract
Purpose Despite the recurrence of earthquakes, responses are usually triggered afterwards, lacking mitigation strategies to diminish risks. Damaged dwellings cannot be immediately reinforced to continue inhabitation, generating disruption. Repairs are usually costly, as large numbers of affected constructions make damage assessment difficult, and post-earthquake reconstruction programmes often lack a heritage-specific approach. This research seeks to address these issues through a methodology based on high-end documentation technologies applied to built heritage and local community engagement. Design/methodology/approach The methodology proposed combines different recording tools to capture social and built environment data, such as interviews, mapping, drone capture, photography and 3D laser scanning, in the pilot case study of Bela, a historical settlement in the seismic region of Kutch in Gujarat, India, affected by the 2001 Bhuj earthquake. This paper particularly discusses aspects of community engagement around data capture and representation processes. Findings The introduction of advanced documentation technologies can help speed up the process of damage assessment, analyse social aspects that are key to a respectful re-construction, and enhance community engagement through visual representations, which are relevant to social acceptance and understanding towards a meaningful introduction and sustained use in earthquake risk management. Research limitations/implications The methodology proposed can inform similar cases in seismic areas and enhance engagement, helping to develop a sense of awareness in the community regarding the need for preparedness in the face of earthquakes. However, there are technical challenges in using advanced recording technologies in terms of equipment accessibility, skills, knowledge and future uses of the data. Social and cultural aspects, such as caste and gender divisions, also implied disparity in accessing the data and relating it to the research team, bringing forward the need to tailor public engagement to achieve inclusivity. Practical implications This study has practical implications. The most relevant one is how the process of carrying out the research served as a way to raise awareness for future seismic events. In this regard, local academic institutions and non-governmental organisations (NGOs) are critical mediators in reaching the community in greater depth, from which to bridge to external and/or governmental agencies based on existing links and trust. Social implications This study also shows social implications. As a case study-based research, the link developed with the local community will help coordinate actions in case an earthquake occurs and increase cohesion within the community towards a joint aim, in this case, to reduce the risk of disasters due to seismic events. Originality/value This paper accounts for a novel approach to documenting buildings in heritage settlements prone to earthquakes that uses the digital record as (1) a basis to assess and intervene in the built environment and better understand how it supports the local community’s ways of living and maintaining buildings; and (2) a platform for local engagement and agency in planning and re-construction as a post-disaster mitigation measure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.014 |
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".