A Social Capital Inventory for Adult Literacy Learners
Notice bibliographique
Résumé
[Abstract] The purpose of this study was to assess the current scholarship on adult learning and social capital with specific attention to research in Canada, the United Kingdom, Australia, and the United States. This review provided the foundation to develop and pilot new measure called the Social Capital Inventory (SCI) in an adult high school custodial training program. Cronbach alphas were computed to assess the internal consistency of the items for the total scale and for each subscale. The total scale showed good reliability with alpha = .88. Results of the pilot study seem to suggest that how we measure social capital may be intrinsic to the adult learning process as Canadian born and immigrant trainees begin to realize the social outcomes of literacy program. [Keywords] adult learning; social capital; literacy; tool development Introduction For countries such as Canada, the United Kingdom, Australia, and the United States, work skills development is key building block towards international competitiveness. Although each of these countries has crafted its own distinct skills strategy, common feature across these national workforce policies is the new attention given to adult Such learning allows workers and trainees to strengthen the skills needed to fully participate in labor market that is being transformed by new technologies. For example, according to Statistics Canada (2010), an estimated ten million Canadians aged 18 to 64 had participated in some form of education or training related to career, job, or personal interest. At the same time, however, recent reports also suggest that despite the importance of adult learning, number of challenges still persist across international contexts (OECD, 2007; Statistics Canada, 2008). Over the past few years, workplace education, essential skills programs, and work-based learning have occupied central place for workers to access and engage in the full range of learning and training opportunities (Taylor, Evans, & Pinsent- Johnson, 2010). Although this continues to be an evolving area in adult learning, there is growing awareness that measuring the economic and non economic returns of these types of investments is difficult to wrestle down. Over the past decades, adult and workplace learning has often been seen through narrow policy lens of preparing for employment and as means for increasing wages and productivity (Riddell & Sweetman, 2001; OECD, 2005; Machin, 2006). However, with the need for greater social inclusion of several Canadian sub groups such as marginalized adults and workers with low skills, it has now become important to look beyond measures of earning and move towards measures of learning (HRSDC, 2009a and b). According to the Organization for Economic and Cultural Development (2006, p. 15) a great deal is known about how much people earn after completing an additional year's schooling, but lot less is known about outcomes society intends education to provide and even less about the unintended consequences of learning. Furthermore, the Canadian Council on Learning (2009) maintains that there are considerable gaps in our knowledge of adult learning and ways for understanding and measuring the non-economic outcomes to Ih addition, the OECD (2005) has acknowledged the fact that human capital theory does link education to economic returns, but there is, as of yet, no widely accepted theory linking education to social outcomes. As suggested by Hudson and Anderson (2006), our understanding of the non-economic returns to learning is vastly underdeveloped (p. 19). Some early evidence does seem to indicate that learning produces social as well as economic returns to individuals firms and society at large. For example, several empirical studies have attempted to show the causal connections between education and health (OECD, 2007). In similar vein, Desjardins and Schuller (2006) suggest that continuous learning over the life course has been linked to everything from economic prosperity to greater political participation. …
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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 | 0,001 | 0,001 |
| 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 | 0,001 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».