No. 26: Social Media, The Internet and Diasporas for Development
Notice bibliographique
Résumé
The recent focus on diasporas by policy-makers researchers has highlighted the rich potential of migrants as a force for shaping development activities in their countries of origin. The study of diasporas in development presents researchers a number of significant challenges. As Vertovec and Cohen suggest, âone of the major changes in migration patterns is the growth of populations anchored ⦠neither at their places of origin nor at their places of destinationâ. The fluid, multi-sited and multi-generational nature of diaspora groupings poses considerable methodological challenges of definition, identification, location, sampling and interviewing.\nAs the nature of African diasporas are constantly in flux so too should the methodologies we use to study them. In practice, traditional approaches lead to the same methodological roadblocks. Census and immigration data (particularly from destination countries) can provide an overall picture of diaspora stocks, flows and locations. However, privacy issues generally preclude these sources from providing disaggregated data at the level of the individual migrant or migrant household. Surveys of diaspora members have therefore become the standard means of collecting information on diaspora characteristics, identities, activities and linkages. This immediately raises a set of problems and challenges. Census data can tell us the size of the population to sample but not who the individuals are, where they live and how to contact them. Without a sampling frame, researchers tend to rely instead on âsnowballâ, âpurposiveâ or âconvenienceâ sampling. This has produced a disproportionate number of studies that rely on key informant and focus group interviews in order to create a profile of diasporas and their development-related activities.\nDiasporas are often geographically dispersed within a country and across different countries. Cost and time constraints and the bias of snowball and convenience sampling lead to a focus on sub-sets. Studies of diaspora members in particular cities or regions are especially common. While sample sizes vary considerably, there is a marked reliance on very small samples, which raises obvious questions about the representativeness and generalizability of the findings.\nThe mail-out survey is still the preferred method of reaching members of a geographically dispersed diaspora, although response rates remain stubbornly low. To contact members of the diaspora, mailing lists are compiled from organizations that keep, and are willing to share, membership lists (such as diaspora organizations, embassies, alumni associations, immigrant service agencies and religious organizations). However, this means an inherent sampling bias since data collected from these individuals and groups has the potential to be skewed towards diaspora members actively engaged with their origin country. This method of âaccessing the diaspora through the diasporaâ is also unlikely to provide much information on âhiddenâ members of a diaspora whose immigration status may be undocumented or uncertain and who are wary of disclosing personal information directly to researchers. Researchers have also noted that members of vulnerable populations such as asylum seekers and refugees might be reluctant to provide personal information due to fear and trust issues.\nTo identify and connect with larger numbers, different strategies need to be adopted. In this context, the potential of the internet has rarely been considered. Since the advent of the internet age, more than one billion people have become connected to the World Wide Web (WWW), creating seemingly limitless opportunities for communication. The past decade has also seen a major increase in the use of the internet by diaspora individuals and groupings. The internet has not only facilitated remittance transfers, but has increased communication among and between diasporas and influenced the formation of diasporic identities. In this context, the potential of web-based methodologies in diaspora research appears promising. The aim of this paper is twofold. First, we argue for supplementing conventional approaches with new methodologies that embrace the connectivity of diasporas, the emergence of social media and the potential of online surveys. Second, we illustrate the potential of this approach through discussion of the methods adopted in our current research on the African diaspora in Canada.
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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) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
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