International migrants in Johannesburg's informal economy
Notice bibliographique
Résumé
Some 70% were men and 30% were women; 96% were aged between 20 and 49 years; 29% had primary schooling or less, almost 40% had some secondary education, 23% had completed secondary school, and 9% had at least some tertiary education.• They came from 27 countries of which 21 were in Africa.The majority were born in SADC countries (65%), particularly Zimbabwe (30%) and Mozambique (14%).Some were from Nigeria (7%), the DRC, Lesotho, and Pakistan (5% each), and India (4%).• At least 46% were asylum seekers, refugees, or permanent residents with permits that allow them to own and operate businesses in South Africa.Another 20% held work permits, mostly Zimbabwean Special Dispensation Permits which again allowed them to operate a business.Another 12% held visitors' permits, while only 12% had no official documentation.• Less than 5% had arrived in South Africa in 1994 or before.Around 80% had arrived since 2000, with a third arriving between 2000 and 2004, 30% between 2005 and 2009, and 15% between 2010 and 2014.Migrant entrepreneurs are often perceived to have advantages in business skills and experience compared to South Africans.At the same time, entrepreneurs in the informal economy, regardless of nationality, are often seen as survivalists without entrepreneurial aspirations and skills.As regards these perceptions, the survey found that:• Over half (56%) of the entrepreneurs had been unemployed before coming to South Africa.However, only 5% were involved in informal entrepreneurial activity and only 2% had owned a business in the formal economy in their home country.• Almost half (47%) had been unemployed in South Africa before starting their business.Just over a quarter had done semi-skilled or unskilled manual work.However, 5% were professional workers, suggesting that the informal economy offers opportunities not always found in the formal economy.• Only a minority of the entrepreneurs had prior entrepreneurial experience in South Africa, with 13% having operated a previous informal economy business and 5% owning a business in the formal economy before starting their current business.• Challenging perceptions that migrant entrepreneurs arrive in South Africa armed with skills that give them advantages over South Africans, 56% said their skills were selftaught, 19% had learned from friends and relatives, and 10% had learned from previ-migration policy series no.71
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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.
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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».