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Record W4385311495 · doi:10.1515/9780773572232-007

Emerging Countries: Opportunities and Challenges

2004· book-chapter· en· W4385311495 on OpenAlexaboutno aff
Jacques de Larosière

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2004
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.004
Scholarly communication0.0090.010
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0160.002

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.

Opus teacher head0.050
GPT teacher head0.239
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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Same venueMcGill-Queen's University Press eBooksSame topicInternational Development and AidFrench-language works237,207