Communicating Sustainability: An Analysis of the 2017 United Nations Sustainable Development Goals Report
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".