Trade, Skill Formation, and Human Development: A Theoretical Note With Reference to Less Developed Economies
Bibliographic record
Abstract
Abstract The context of skill creation and its development is fundamental to sustainable economic growth with vertical improvement in well-being. Now when it comes to the case of less developed countries, the implication of international trade in skill formation takes an idiosyncratic shape so far as our concern: a dearth of skill education and lack of evenness in access to skill education due to the underlying rampant and pronounced economic inequality (i.e., inequality in income and wealth) among people as what is quite typical. Against this backdrop, this chapter seeks to develop a general equilibrium model in line with Jones (1965 & 1971) and Beladi and Marjit (1996) to address how leveraging of foreign trade through technological modernization of exports may work toward skill formation in less developed economies with technological dualism, informalization, and disguised unemployment. Besides, this chapter brings to glare how benefit of such modernization toward skill development stands out to be weighed against a potential worsening of distributive justice in terms of rise in wage gap between skilled and unskilled workers. Moreover, this chapter seeks to overhaul the implication of liberalization of labor market in terms of dilution of minimum wage standard for human development. Thus, the bottom line is that comes up here forth that export modernization in name of improving external competitiveness and thereof attaining effective trade openness can promote skilled human but only risking an exacerbation of wage inequality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".