Differential OER Impacts of Formal and Informal ICTs: Employability of Female Migrant Workers
Bibliographic record
Abstract
Information and communication technologies aid marginalized groups in seeking social support, building proximate networks, and improving employment opportunities. However, one key factor that is understudied in the literature is the impact of open education resources (OER) on the employability of marginalized groups. This study focuses on open and distance learning in the context of low-income female migrant domestic workers as a marginalized community. Specifically, we assessed the differential effects of two types of communication: informal OER resources (e.g., social media, mobile calling, texting) and formal OER resources (e.g., classroom prescribed learning tools and lectures) on specific development outcomes of functional literacy and perceived employability. A survey was conducted amongst female migrant domestic workers (n=100) enrolled in the Indonesian Open University in Singapore. Results indicate that access to OER resources via computers in the formal context of institutional learning, when combined with employability awareness, had a significant influence on livelihood outcomes, i.e., perceived employability. However, this did not lead to actual improvements in learning – functional literacy. Instead, actual learning improvement was influenced by digitals skills enabled by mobile phones and computers. The study concludes with a discussion on the policy implications for digital skills training via mobile devices for marginalized populations to bolster the positive effects of OER on livelihood outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".