Realities of economic livelihood strategies of urban poor Malay families during Covid-19 pandemic / Nor Hafizah Mohamed Harith and Nur Fatima Aisya Jamil
Bibliographic record
Abstract
Rakodi and Llyod-Jones (2002) explain that vulnerability is a threat to the urban poor. It includes the ability of the poor to recover or be resilient to overcome shocks, stresses, and longterm socio-economic life difficulties. However, Moser (1996) argues that the ability to avoid or reduce vulnerability depends on the capacity of the poor to manage and transform these assets into income, food, or other necessities to sustain their livelihood. Nevertheless, the outbreak of COVID-19 poses a significant threat and economic vulnerability for the urban poor in sustaining their livelihood. The first case of COVID-19 was brought into Malaysia by Chinese nationals in February 2020. As of 24 September 2021, Malaysia reported more than 2 million COVID-19 cases (Ministry of Health Malaysia, 2021). The economic catastrophe of the COVID-19 is tremendous. The latest Gini Coefficient score released by the Department of Statistics, Malaysia (2020) measures income and wealth inequality within a country, increased by 0.008 index points from 0.399 in 2016 to 0.407 in 2019, indicating the income gap between households is widening. As of today, the COVID-19 and movement restriction had adversely affected the poor urban. The socio-economic factors of approximately half of the low-income households living in the capital city's public flats worsened further in the fourth quarter of 2020 (UNICEF & UNFPA, 2020).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".