Social repair and structural inequity: implications for disaster recovery practice
Bibliographic record
Abstract
Purpose – This paper introduces a model of social repair to the language of disaster recovery that potentially provides a new way of conceptualizing reconstruction and recovery processes by drawing attention to the dismantling of structural inequities that inhibit post-disaster recovery. Design/methodology/approach – The paper first engages with the current discourse of vulnerability reduction and resilience building as embedded within a distinct politics of post-disaster recovery. The concept of social repair is then explored as found within post-conflict and reconciliation literature. For application within the context of natural disasters, the concept of social repair is modified to have evaluative and effectiveness significance for disaster recovery. A short case example is presented from post-flood Pakistan to deepen our understanding of the potential application and usage of a social repair orientation to disaster recovery. Findings – The paper recommends that the evaluative goals of post-disaster recovery projects should be framed in the language of social repair. This means that social relationships (broadly defined) must be restored and transformed as a result of any disaster recovery intervention, and relationship mapping exercises should be conducted with affected communities prior to planning recovery interventions. Originality/value – Current discourses of disaster recovery are rooted within the conceptual framings of reducing vulnerabilities and building resilience. While both theoretical constructs have made important contributions to the disaster recovery enterprise, they have been unable to draw sufficient attention to pre-existing structural inequities. As disaster recovery and reconstruction projects influence the ways communities negotiate and manage future risk, it is important that interventions do not lead to worsened states of inequity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".