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Record W3046978633 · doi:10.4324/9781003023470-15

Growing significance of regional trade agreements in opening public procurement

2020· book-chapter· en· W3046978633 on OpenAlexaboutno aff
Jean Heilman Grier

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementGovernment procurementBusinessInternational tradeNegotiationGovernment (linguistics)Free tradeInternational economicsEconomicsPolitical scienceMarketing

Abstract

fetched live from OpenAlex

The Agreement on Government Procurement (GPA) under the World Trade Organization (WTO) has attracted 48 members and serves as a model for other trade agreements with procurement commitments. This chapter compares the role of the GPA and Regional Trade Agreements (RTAs) in opening public procurement markets. It starts with a brief overview of the treatment of government procurement in the development of the international trading system, from its exclusion for more than three decades to incorporation of procurement commitments in plurilateral agreements that provide access to procurement markets only for the signatories. In contrast to the US, the EU is pursuing an ambitious trade agenda that includes negotiating RTAs with GPA partners, especially Canada and Japan, which expanded significantly commitments they have taken under the GPA. The chapter concludes that, with constraints on broadening GPA membership and the proliferation of RTAs that open public procurement, RTAs likely offer the greater potential of leading the expansion of international procurement commitments.

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)0.003
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.004
Scholarly communication0.0100.008
Open science0.0010.005
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0220.007

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 head0.084
GPT teacher head0.245
Teacher spread0.161 · 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
GenreEmpirical

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

Citations1
Published2020
Admission routes1
Has abstractyes

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