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Record W2810232353 · doi:10.20381/ruor-22065

Are the Sustainable Development Goals Realistic and Effective: A Qualitative Analysis of Key Informant Opinions

2018· article· en· W2810232353 on OpenAlexaff
Annalise Mathers, Raywat Deonandan

Bibliographic record

VenueuO Research (University of Ottawa) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSustainable developmentThematic analysisPolitical sciencePublic relationsCorporate governanceGlobal healthFocus groupQualitative researchPsychologyManagement scienceBusinessHealth careSociologyEconomicsSocial scienceMarketing

Abstract

fetched live from OpenAlex

The UN Sustainable Development Goals (SDGs) were devised in part to help define the international development funding agenda for future decades. This study sought to explore the challenges and strengths of the SDGs, with respect to their ability to effectively address current and future global health issues. Active researchers and opinion leaders in global health research were interviewed about their opinions on the future of global health, with particular attention to the likely impact of the SDGs on individual research programs. According to thematic analysis, respondent felt that the SDGs should focus more on the development of good governance structures, address corruption and tax systems to develop more comprehensive health structures and financing and embody a more holistic approach to global health.

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)0.044
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0070.009
Scholarly communication0.0070.007
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.415
Teacher spread0.339 · 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 designQualitative
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

Citations7
Published2018
Admission routes1
Has abstractyes

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