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Enregistrement W3092885275

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

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

Notice bibliographique

RevueNottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2019
Typedissertation
Langueen
DomaineHealth Professions
ThématiqueFood Security and Health in Diverse Populations
Établissements canadiensnon disponible
Organismes subventionnairesTrent UniversityNottingham Trent University
Mots-clésFood securityLivelihoodSocial capitalPsychological resilienceDevelopment economicsNatural capitalFood insecurityCoping (psychology)Economic growthPovertyBusinessGeographyPolitical scienceSocioeconomicsEconomicsPsychologyAgricultureSocial psychologyEcosystem services
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,043

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0020,002
Science ouverte0,0010,003
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,066
Tête enseignante GPT0,347
Écart entre enseignants0,281 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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