Health, Quality of Life and GDP: An ASEAN Experience
Bibliographic record
Abstract
Governments all over the world would like to improve the well being of its citizen. One aspects of well being can be seen through the quality of life (QoL) a person enjoys. Measuring and determining what is QoL is not an easy task. In this paper, using per-capita income as the yardstick for QOL, and the role of government through the way it spend its public money would be able to tell how ASEAN governments’ expenditure influences the QoL. Particularly government expenditure on health signifies the commitment of a government in improving the QoL. QoL can also be seen through the availability of health care in country. When health care improves, life span and earning abilities will raise too. It is the government’s responsibility in providing the best health care to the people. Government’s commitment can be gauged through the amount it spends on these items. At the same time government spending would bring spillover effects in terms of raising per-capita income, which reflects the QoL. Using data on government expenditure of ASEAN countries for the last 15 years, it would be possible to determine on what extent expenditures on health do influence the per-capita income. Initial findings reveal that the impact of both these government expenditures on per-capita income is not the same for all the ASEAN countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".