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Record W4285483051 · doi:10.5089/9781513571577.001

Meeting the Sustainable Development Goals in Small Developing States with Climate Vulnerabilities: Cost and Financing

2021· article· en· W4285483051 on OpenAlex
Aleksandra Zdzienicka, Dinar Prihardini

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIMF Working Paper · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
FundersInstitute of Environmental Science and ResearchEuropean CommissionGlobal Environment FacilityUNICEFAfrican Development Bank GroupDepartment for International DevelopmentUnited Nations Educational, Scientific and Cultural OrganizationCanada Excellence Research Chairs, Government of CanadaInter-American Development BankWorld Health Organization
KeywordsClimate FinanceDiversification (marketing strategy)BusinessActivity-based costingWork (physics)Climate resilienceSustainable developmentClimate changeResilience (materials science)Developing countryNatural disasterFinanceEnvironmental resource managementNatural resource economicsEnvironmental planningEconomicsEconomic growthGeographyPolitical scienceAccounting

Abstract

fetched live from OpenAlex

Small Developing States (SDS) face substantial challenges in achieving sustainable development. Many of these challenges relate to the small size and limited diversification of their economies. SDS are also among the most vulnerable countries to the impact of climate change and natural disasters. Meeting SDS sustainable development goals goes hand-in-hand with building their climate resilience. But the additional costs to meet development and resilience objectives are substantial and difficult to finance. This work adapts the IMF SDG Costing methodology to capture the unique characteristics and challenges of climate-vulnerable SDS. It also zooms into financing options, estimating domestic tax potential and discussing the possibility of accessing ‘climate funds.’

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.212
Teacher spread0.182 · 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