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Record W4302012116 · doi:10.32920/ryerson.14655135

Communicating Sustainability: An Analysis of the 2017 United Nations Sustainable Development Goals Report

2022· preprint· en· W4302012116 on OpenAlexaff
Linnea Franson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsProsperitySustainable developmentSustainabilityMillennium Development GoalsFraming (construction)Political sciencePublic relationsPovertyGeographyEcology

Abstract

fetched live from OpenAlex

This MRP provides an analysis of the communication strategy of the United Nations (UN) Sustainable Development Goals (SDG) in an attempt to better understand the role of communication in sustainable development. In 2014, UN Member States put forth the SDGs, following the Millennium Development Goals (MDGs), serving as renewed targets for the global population from 2015-2030 (Miller-Dawkins, 2014). The SDGs and their ambitious yet transformational agenda aim to promote prosperity for all of humankind, while protecting our planet. This MRP establishes how the goals of the SDG campaign are being communicated in the Sustainable Development Goals Report 2017 (SDG Report 2017). The report is released annually based on the latest available data and provides a snapshot of the efforts to date. In addition to exploring how the report is designed and communicated, this MRP will result in a list of recommendations for future international communication initiatives. These recommendations are informed by the literature review touching on the changing nature of international development and communication while also applying and linking three key areas of theoretical discourse: Knowledge Translation, Hofstede’s (2003) cultural dimensions, and concepts of Framing Analysis.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0020.006
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.048
GPT teacher head0.338
Teacher spread0.290 · 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 teacher head, not a consensus.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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