Working Children and Knowledge of Right to Education: A Study of Child Labour in Sabah, Malaysia
Bibliographic record
Abstract
In many countries of the world child labour persists despite the existence and implementation of laws and regulations to eliminate the problem. In many instances children are preferred as workforce because they are easier to manipulate, intimidate, abuse and exploit due in part to their inexperience and relative immaturity. There are many reasons why children work. However, their labour participation means that they are denied or deprived of their right to education, which is crucial to their future prospects, personal development and directly or indirectly to the development of a country. They are not attending school as they should or are not spending enough time on educational development. A study was conducted in 2007-2008 in Tawau, Sabah, Malaysia to determine if the working children are aware of their right to education and if they feel deprived not attending school. It is also to identify reasons they work and if they would return to school if given the opportunity to do so. A total of 26 child labourers aged 9 – 18 years were sampled and interviewed for the purpose. This paper discusses the findings of the study. From the study it can be concluded that there are many reasons that caused many children to work, which in turn have deprived them of their education. Without education their future would be bleak. This is because only education can help change and improve their lives and their future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".