Bibliographic record
Abstract
T his article focuses on an important learner—the adult with low literacy skills. Across the global enterprise of adult education, practitioners need to become more aware of the characteristics of this marginalized group of adults. Therefore, the intention of this article is to highlight the profile of this learner through the lens of different sociocul-tural theories to help guide decisions for effective teaching into the future. Learning contexts and adults with low literacy skills are two main themes in the scholarly literature. Drawing from Organization for Economic Co-operation and Development (OECD) and other country reports, Werquin (2010 ) suggests that the definition of learning context is subject to debate. However, formal education and informal learning may be considered the two extremities of a learning continuum, with nonformal learning situated somewhere in between, depending on national and local perspectives. In this overview, three learning contexts provide the backdrop to profile adult learners with low literacy skills, each through a theoretical lens that helps explain the teaching and learning process. The first profile—the adult basic education (ABE) learner in the formal setting—is viewed through a motivational framework for culturally responsive teaching and a social capital theory. The adult with low literacy as a lifelong learner engaging in informal learning is the second profile and is explored through social constructivism. The third relates to essential skills training of the adult worker as seen through social cognitive theory (Essential Skills Ontario, 2012 ).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".