A Data Visualization Analysis of the GDP and Other Expenditures of Some of the G20 Countries
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
This is a data visualization project that aims to present and visualize some relevant statistics, namely the gross domestic product (GDP), the military spendig, the education spending and the healthcare spending of some of the G20 countries during the 2011-2015 time range. The countries analized are the following: Australia, Brazil, Canada, France, Germany, Italy, Japan, South Korea, Mexico, Turkey, the United Kingdom, and the United States. Initially, I wanted to include more countries and analyze a more recent time range, but the education spending dataset had many missing values, and consequently I had to pick the countries based on data availability. All the datasets this project is based on come from the World Bank Open Data Catalog with the exception of the education spending dataset which was retrieved from the website of the Organisation for Economic Co-operation and Development (OECD). I have uploaded some screenshots of my charts that outline the some of the findings of this project:
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| 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 it