Evaluating the economic disparities in the world: Sentiment Analysis on Central Bank Speeches from Third World and First World Countries
Bibliographic record
Abstract
This paper provides an evaluation of the financial and simple sentiment of central bank speeches from 2004- 2019. The speeches are categorized into two different sub-groups with three different countries for each one, respectively: Zambia, Barbados, and Sri Lanka for the third world [developing] group and France, Canada, and Japan for the first world [developed] group. This paper attempts to answer the following questions: (i) What do the sentiments of each country or sub-group tell us about their economic condition? (ii) How does the sentiment relate to the countries' contextual economic growth or decline? (iii) How do the central or federal banks of the country portray the economy? Using natural-language processing, more specifically BERT transformer models and modern NLP methods, a synthesized evaluation is created of what economic linguistics from central banks can reveal about the world's economy.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".