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Record W4393022118 · doi:10.1596/41221

IFC and Ireland, Partners in Private Sector Development

2024· book· en· W4393022118 on OpenAlexaboutno aff

Bibliographic record

VenueWashington, DC: World Bank eBooks · 2024
Typebook
Languageen
FieldSocial Sciences
TopicEU Law and Policy Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrivate sectorBusinessEconomic growthEconomics

Abstract

fetched live from OpenAlex

IFC’s main government counterparts are the Ministry of Finance, Irish Aid, and Enterprise Ireland. In FY19-23, Ireland provided cumulative funding of over five million in support of IFC Advisory Services, of which close to two million was committed in FY23. To date, Irish funding has supported several strategic IFC initiatives aimed at promoting private sector development in emerging markets, including in conflict-affected states in Africa, as well as gender equality and inclusion. In September 2019, IFC listed its first Canadian dollar green bond on the Euronext Dublin, the first time IFC has listed a green bond on Dublin’s stock exchange. In October 2018, IFC and the Ireland Strategic Investment Fund (ISIF) signed a Memorandum of Understanding to work together to generate growth opportunities for Irish companies in emerging markets, with an initial focus on the food and agriculture sectors.

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)0.002
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.068
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.002
Scholarly communication0.0110.008
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0680.045

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 head0.029
GPT teacher head0.304
Teacher spread0.275 · 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
Published2024
Admission routes1
Has abstractyes

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