Work-integrated (adult) learning: Un-stigmatizing blue-collar adult learners in Singapore by embracing visibility
Bibliographic record
Abstract
‘Continuous meritocracy’ was introduced in Singapore to redefine the concepts of talent and ability in Singapore society. This expanded meaning of meritocracy serves as another way to further support the SkillsFuture Singapore movement (Skillsfuture Singapore, 2023b), which was launched in 2016. ‘Continuous meritocracy’ complements Work-Integrated Learning (WIL) programs, which were to provide adult learners opportunities to integrate practical work experiences with academic learning. However, to fully operationalize WIL in the domain of adult learners, this paper points out that it is vital for the Singapore government and the different stakeholders to endorse the different forms of successes by making them more visible in the society. Utilizing Pierre Bourdieu’s key theoretical concepts, this paper discusses the relationship between blue-collar adult learners’ dispositions and WIL and proposes an ecosystemic approach that is based on work-integrated (adult) learning (WIAL) to transform the Singapore blue-collar workers’ habitus with the aim to visualize ‘continuous meritocracy’ at the ground level.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".