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Record W2939670165

Implementation Frameworks for International Summits or Conferences

2018· article· en· W2939670165 on OpenAlexaboutno aff
Zenobia Ismail

Bibliographic record

VenueFigshare · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceBusinessPolitical scienceProcess management
DOInot available

Abstract

fetched live from OpenAlex

This rapid literature review examined the accountability mechanisms used by seven global summits or conferences: the United Nations Conference for Sustainable Development (2012), the World Health Organisation Ministerial Conference on Ending TB (2017), the Nuclear Security Summit (2016), World Conference on Indigenous People (2014), One Planet Summit (2017), the Montréal Protocol (1997), and the United Nations Conference on Trade and Development (2015). In addition, two other global agreements were reviewed: Sustainable Energy for All (2011) and the Tokyo Mutual Accountability Framework. There is no grey literature on this topic and the only articles in the academic, peer reviewed literature relate to examining the effectiveness of the Montréal Protocol. Therefore, the review relied on an assessment of the processes or frameworks for reporting and monitoring which are described in the conference or summit documents. Some of the documents were not up to date. Moreover, it was not clear whether the accountability framework was put in place at the start of the conference or summit or if it was adopted later. The review of the accountability frameworks used in the aforementioned conferences, summits or global agreements ascertained the following findings. First, while an organisational structure is necessary for implementing the resolutions or commitments, financial support is also necessary to incentivise implementation especially for developing countries. Second, measurable targets must be set at global, regional and country level. Targets and reporting can be disaggregated to reveal discrepancies according to age or sex, e.g., such disaggregation is required for monitoring progress towards eliminating tuberculosis. Third, countries report their progress by providing a country report. Reporting progress and monitoring are best facilitated if there are other agreements, conventions or protocols that facilitate such reporting. Monitoring and reporting tends to be more robust when there is a designated organisational body that manages the resolution. Finally, the documentation relating to the Nuclear Security Summit and the One Planet Summit is not explicit with regard to which entity is responsible for monitoring implementation and progress.

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)0.176
metaresearch head score (Gemma)0.233
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: Methods · Consensus signal: none
Teacher disagreement score0.176
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1760.233
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0140.012
Science and technology studies0.0060.009
Scholarly communication0.0210.019
Open science0.0070.018
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0220.003

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 head0.517
GPT teacher head0.612
Teacher spread0.095 · 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
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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