The NextWave of Expeditious Economic Growth (China, India, Brazil, South Africa and Russia)
Bibliographic record
Abstract
Being a finance student what amazes me, and should really astound every \nindividual who has the slightest economic sense, is the nature and sheer pace of \nthe changing dynamic of the economies of this world. It will have a profound \nimpact on almost every financial decision made and in fifty years (or even \nless), the list of the world’s ten largest economies will look very different. The \nmight of United States of America (USA), Japan and Western European \ncountries like Germany and United Kingdom will slowly diminish and pave \nway for the next surge of dominance in the form of countries like China, India \nand Brazil. This notion, to my mind, is no longer a matter of opinion but \nincreasingly becoming a matter of fact. It is clear when India grows at around \n8% per annum while most industrialized economies contract. It is clear when \nChina manufactures more and more of what the world consumes. It is clear \nwhen the President of the USA tells students that ‘you are competing with the \nkids not only in your own country but in India and China’. \nThis dissertation attempts to highlight this change by reflecting on the many \nfacets of this proposition and giving an overall view of this exciting alteration \nin the world economy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.011 |
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".