Thailand Economic Monitor, January 2019 : Inequality, Opportunity and Human Capital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The outlook for the global economy has \n darkened amid elevated trade tensions. International trade \n and investment are moderating, trade tensions remain \n elevated, and financing conditions are tightening. Global \n growth is projected to moderate from a downwardly revised 3 \n percent in 2018 to 2.9 percent in 2019 and 2.8 percent in \n 2020-21, as economic slack dissipates, monetary policy \n tightens in advanced economies, and global trade gradually \n slows (World Bank Global Economic Prospects, January 2019). \n Despite external shocks to trade and tourism, growth of the \n Thai economy is estimated to have accelerated to 4.1 percent \n in 2018. The economy proved to be resilient in the face of \n strong global headwinds due to strengthening domestic demand \n stemming from an upswing in private consumption and private \n investment. Domestic consumption expanded by 5 percent in \n 2018Q3, posting the highest growth rate in 22 quarters in a \n low-inflation environment and record-low unemployment. In \n addition, private investment grew by 3.9 percent in the \n third quarter supported by increased spending on \n construction, machinery and equipment. Strong domestic \n demand offset partially adverse global factors—the China-US \n trade dispute—as well as domestic and idiosyncratic \n factors—such as the Phuket boat tragedy and the high-base \n effect of gold exports. The Thai economy also owed its \n resiliency to strong and stable macroeconomic fundamentals.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.010 |
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 it