Integrating welfare economics in social work curriculum: a Malaysian case
Bibliographic record
Abstract
This paper makes a case for why welfare economics should be integrated and taught in social work courses, taking Malaysia a case in point. This is mainly a conceptual paper and secondary data are used to further support the arguments. Commencement of professional social work in Malaysia dates to 1946, to address the socio-economic problems of the Malaysians and migrants at that time. Social workers need a multi-pronged approach that is crucial to address the human problems that includes psychological, social, political, cultural and economic at micro and macro levels. Most of the problems referred to the social workers stem from poverty, unemployment, low access to material resources and corrupt governance practices coupled with unjust economic policies. Keeping in view the diverse economic needs and strengths of the clients referred to social workers, it is necessary that social workers are equipped with appropriate skills that include broader understanding about political economy. This paper argued that integration of welfare economics in the social work curricula is an urgent need considering the Malaysian economic development, austerity measures and the proactive role that social work as a human rights profession could play in the Malaysian society.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".