Intellectual capital, social policy, economic development and the world evolution
Bibliographic record
Abstract
The aim of this paper is to study the relation between intellectual capital (IC) and economic development (ED). The analysis presented aims to have both theoretical and practical relevance. The main hypothesis assumed is that IC, although fundamental, is not sufficient to assure the existence of ED. Instead, argues that, for a developing country to become a modern economy some facts have to happen. Those facts are: a process of stable political democratisation; a process of economic stabilization; a process of economic and political integration; a process of investment in development tools, like physical capital and intellectual capital. Furthermore, it is shown that the process of investment in IC implies the development of active social policies, but it is difficult for a developing county to make those social policies by itself. In consequence, in the future, due to the need of economic and political stability, the growing globalisation and the lack of development resources (IC, infrastructures, etc.) in developing countries, some Economic and Political Unions (EPUs) may emerge, or consolidate. Those EPUs are the European Union, the American Union (around Canada and the USA), the Asian Union (centred in Japan), the African Union (based in South Africa), the Oceanic Union (beginning with Australia and New Zealand), and the Arab Union (mainly constituted by Middle East oil producers).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".