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Record W3092885275

Household resilience against food Insecurity in areas of protracted conflicts: a Libyan study.

2019· dissertation· en· W3092885275 on OpenAlexfundno aff
Rmm Swesi

Bibliographic record

VenueNottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2019
Typedissertation
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsFood securityLivelihoodSocial capitalPsychological resilienceDevelopment economicsNatural capitalFood insecurityCoping (psychology)Economic growthPovertyBusinessGeographyPolitical scienceSocioeconomicsEconomicsPsychologyAgricultureSocial psychologyEcosystem services
DOInot available

Abstract

fetched live from OpenAlex

Recent estimates provided by UN institutions indicate that over 820 million people are currently suffering from food insecurity worldwide. Conflict has been widely identified as one of the key causes of such persistent and high level of global food insecurity, particularly in the Middle East and North African (MENA) region, including Libya. It is, therefore, important to know how to overcome this problem. Recently, ‘resilience-building’ has been identified by many development institutions around the world as a strategy to improve food security in conflict-affected areas. However, little was empirically known what makes households resilient against food insecurity in areas of protracted conflicts. In this thesis, I explored this question based on research in Libya. \n \nDrawing on a range of literature, especially the Sustainable Livelihoods literature, I developed an analytical framework. In this framework, resilience was defined as the ability of a household to maintain an appropriate level of food consumption (access) during conflict times. It was proposed that this ability to be resilient would depend on nine factors: exposure-sensitivity to conflicts, five types of assets (natural capital, physical capital, financial capital, human capital and social capital), coping strategies, access to basic services (ABS), and social safety nets (SSN). \n \nA mixed-methods approach was used in the research. Data were collected through two phases – a qualitative phase and a quantitative phase. The purpose of the qualitative phase was to understand the contexts in Libya, including the nature of the conflicts and its effects on household food security; the nature of assets important in Libyan context; the strategies households used to cope with conflicts and food insecurity; and the nature of the ABS and SSNs relevant to Libya. For this, data were gathered through 55 semi-structured interviews as well as field observations and conversations. The data were analysed qualitatively using the NVivo software. \n \nThe findings from the qualitative phase were then fed into the design of the quantitive part of the research. In the quantitative phase, survey data were collected from a sample of 320 households. A structured questionnaire was used in data collection. The questionnaire data were analysed using the software SPSS versions 25 and 26. Food security was measured using the Food Consumption Score (FCS) and the Household Food InsecurityAccess Scale (HFIAS). Index scores were created for both FCS and HFIAS according to the guideline in the literature. For the nine explanatory variables, index scores were also created using descriptive statistics and Principal Component Analysis. To determine the effects of these nine explanatory variables on food insecurity resilience, binary logistics regression analyses were performed. \n \nResults from both the qualitative and quantitative phase confirmed a significant decline in households’ food security during conflict times, compared to the pre-conflict times. The result of the qualitative phase suggested that all the factors in the proposed analytical framework were important for household food security. However, quantitative analyses showed that only social capital at time t (pre-conflict) had a statistically significant positive effect on resilience against food insecurity during the major conflict in 2011 (time t+1). To analyse resilience in time t+2, two logistic models were created – effects of the nine explanatory variables that households possessed in time t, and time t+1. The results of the first model indicated that household natural capital in time t had a significant positive effect on resilience in time t+2. The result of the second model indicated that household resilience in time t+2 was significantly affected by three variables – natural capital, financial capital and social capital in time t+1. Most of these significant effects were, however, found in the models in which food security was measured as FCSs. \n \nThe main conclusion of this research is that assets play important roles in household food security resilience. The findings also lead to the conclusion that the type of assets that can affect household resilience also depends on which conflict time is taken into analysis and how the variable “food (in)security” is measured. These suggest that, for resilience building in areas of protracted conflict, it is important to identify which assets are important. Development agencies and institutions should then focus on protecting and improving those assets. It is also important for developing agencies to use appropriate tools for assessing and monitoring “food (in)security”, since the results may be different based on which tools are used.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.066
GPT teacher head0.347
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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