Notice bibliographique
Résumé
Introduction Environmental migration has received increased scholarly and policy attention in the last decade. Though environmental drivers have always played a role in migration movements, the number of natural disasters has seemed to be on the rise, and their severity appears to be worsening, possibly due to the first impacts of climate change. Research on environment-related migration flows in recent times has shed new light on the linkages between environmental changes and migration. History shows numerous examples of migrations associated with environmental changes and disasters. In 1755, an earthquake destroyed most of Lisbon, inducing mass population displacements towards other parts of Portugal, with some of those displaced returning to the city later (Dynes 1997). The Dust Bowl migration in the US is another classic example of mass migration associated with environmental disaster. In that case, severe drought and soil-depleting agricultural techniques resulted in dust storms that pushed populations westward. Thousands of farmers from Oklahoma, Texas and Arkansas had no choice other than to sell their farms and move in the 1930s. The environmental ‘push’ factors are obvious in this migration decision, but it should be stressed that other socio-economic factors were at work as well. The migration took place within the context of the Great Depression (Hansen and Libecap 2004). Furthermore, the prospect of a better life in California played a crucial role as a ‘pull’ factor (Gregory 1991). More recently, massive population displacements were triggered by catastrophes such as the Sumatran tsunami in 2004, hurricane Katrina in 2005 and cyclone Nargis, which ravaged Burma in 2008. These disasters raised public awareness about the fate of a new kind of ‘refugee’. In addition to those displaced by sudden events, many more are also displacements by slow-onset events. Most of these are related to climate change: villages resettled in the South Pacific islands to escape the rise in sea-level, farmers and pastoralists moving to cities because desertification threatens their livelihoods in sub-Saharan Africa and Northern China, and Inuit communities displaced by the melting of the permafrost in Alaska. These population movements are not alike, but all can be presented as ‘environmental migration’ and envisioned through this lens.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,091 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».