Bibliographic record
Abstract
Social Weather Stations (SWS) is a private, non-profit, and non-partisan research institute that regularly conducts scientific surveys on various social, economic, and political dimensions of the quality of life of the Filipino people. Its Social Weather Reports stem from a series of nationally representative surveys which were semi-annual in 1986-1991 and have been quarterly since 1992. The Social Weather Reports represent the enlightenment approach to the application of social indicators in a democratic setting. Their indicators of economic well-being include self-rated poverty (SRP), self-rated food poverty, and hunger, measured at the household level. In terms of data points, the quarterly SRP statistics are 12 times as many as the official poverty statistics, which apply monetary poverty lines to triennial surveys of family income. The incidences of SRP are invariably much larger than those of official poverty, which use unrealistically low poverty lines when compared to selfrated thresholds for poverty and food poverty. The time trends of SRP are compatible with official poverty, when matched contemporaneously. They show significant volatility in poverty, not only year to year, but also quarter to quarter. Aside from economic deprivation of households, the Social Weather Reports include the subjective assessments of adults as to whether they have gained or lost in personal quality of life in the past and whether they are optimistic or pessimistic about it for the future. Despite steady growth in per capita Gross National Income, losers regularly outnumbered gainers for decades, but gainers have been dominant since 2014. The Social Weather Reports amply demonstrate that survey-based subjective indicators are more practical and realistic means of monitoring economic well-being than orthodox economic indicators stemming from the National Income Accounts.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".