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Record W258096330

A Social Capital Inventory for Adult Literacy Learners

2012· article· en· W258096330 on OpenAlexaboutno aff
Maurice Taylor, David L. Trumpower, Ivana Pavić

Bibliographic record

VenueInternational Forum of Teaching and Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSocial capitalWorkforceAdult educationScholarshipScale (ratio)LiteracyPsychologyCronbach's alphaPolitical scienceMedical educationPublic relationsPedagogyEconomic growthSociologyMedicineGeographyEconomicsSocial scienceDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

[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. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.453
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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