THE SOCIAL AND SPATIAL DIVISIONS OF PRECARIOUS LABOR
Notice bibliographique
Résumé
The dissertation is composed of four manuscripts, positioned within the field of economic geography. Manuscript one broadly examined how precarious forms of employment (PFEs) are spatially patterned within multiple scales and across a range of geographies. The results suggested that different PFEs exhibited distinct spatial patterns across space and scale. For example, temporary and involuntary part-time work was more prevalent in Atlantic Canada and became gradually less prevalent moving westward. In contrast, part-time employment and employment in multiple jobs were more common in western Canada than in central and Atlantic Canada. The results also confirmed that all PFEs (except for involuntary-part-time work) were more common in rural and small-town areas, and less common in large urban areas. Second, using logistic regression models, results showed that the prevalence of PFEs was reinforced by factors such as immigration status, gender, age, education, and income. These models further confirmed that spatial patterns of PFEs were robust in finer scales i.e. CMAs (census metropolitan areas) and urban/rural geographies even when controlling for socio-demographic and socio-economic effects. Manuscripts two and three builds on the findings in manuscript one by examining how PFEs are spatially patterned across social locations of gender and immigration status, respectively. Results showed that the east-west and urban-rural patterns observed in manuscript one were partially distorted when the analyses were disaggregated by gender and immigration status. The robustness of these spatial distortions was confirmed using logistic regression models. The fourth manuscript sought to understand the spatial characteristics influencing the spatial variations of temporary employment using ordinary least squares (OLS) regression models. Key findings revealed that CMA/CAs (census metropolitan areas/census agglomerations) characterized by large shares of manufacturing, utility, and management occupations were significantly negatively associated with temporary employment. Conversely, CMA/CAs with high shares of sales and service occupations were positively associated with temporary employment. Generally, population characteristics (measured by metropolitan areas characterized by a high share of Asian immigrants, low-income earners, and employment insurance beneficiaries) contributed more to explaining positive temporary employment estimates than industry characteristics.
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) | 0,015 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».