A Human Security Approach to Post-Disaster Rehabilitation and Reconstruction : a Case Study of the 2003 Bam Earthquake, Iran
Bibliographic record
Abstract
Chapter 1 Introduction: 1 1.1 Introduction 1 1.2 Research question and Hypothesis 3 1.3 Research methods 4 1.3.1.Qualitative research: 4 1.3.2.Research Process in Bam: 9 1.4 Purpose of research: 11 1.5 Scope and limitation: 12 1.6 Structure of the thesis: 14 Chapter 2 Concepts, Theories and Research's Framework 17 2.1.Security: a conceptual/historical narrative 18 2.1.1.Conception of Security 18 2.1.2.A Historical Narrative through Various Security Schools: 2.1.2.1 From State Security/Sovereignty… 27 2.1.2.2.To Human Security 49 2.1.3.A Definition of Security through 'Authorization' 64 2.2.Human security framework on disaster analysis 78 2.2. 1.A review of theories of disaster study 78 4.1.Emergency, relief and rescue operations 119 4.2.The international context and mobilization of civil society and domestic NGOs125 Chapter 5 Renegotiation of human security 138 5. 1. Reconstruction failures symbolize political change 138 5.1.1.Reconstruction Plan and practice 138 5.1.2Housing Foundation and Bam reconstruction 144 5.2.Government's social rehabilitation policies: 160 Chapter 6 Dynamics of Historical Learning/Local Knowledge 170 6.1.Traditional systems and livelihood security in Bam 170 6.1.1.Heritage components 171 6.1.2.Qanat Systems (Ancient Irrigation systems) 181 6.2.People's coping strategies and perception about the disaster and reconstruction 185 6.2. 1. Traditional systems of coping strategies 185 6.2. 2. Social grassroots, local NGOs and civil society: capacity building 192
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".