Bibliographic record
Abstract
Sylvia Ostry was Canada's chief trade negotiator throughout the years of the Uruguay Round.She observed that "these 'new issues' [trade in services, particularly those based on communications and information technology] represent a fundamental transformation in the process of trade liberalization.They involve change in domestic regulatory and legal systems embedded in the infrastructure of national economies ... The degree of intrusiveness into domestic sovereignty bears little resemblance to the shallow integration of the gatt."Emerging countries have specific characteristics.They are "developing countries," but they are also characterized by more or less efficient market economies and by access to international financing.The other developing countries -generally the poorest -are those that have not yet reached the emerging stage (mostly located in Africa and certain parts of Asia).In this chapter I answer the question "how are emerging countries faring?" by addressing two related questions:• How have emerging countries taken advantage of globalization over the last thirty years?• What is the current situation of these countries, and in particular what are the risks they run in relation to the worldwide economic slowdown and the current instability of financial markets?In my conclusions I attempt to list future challenges facing these countries as well as the opportunities open to them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".