Discourse Surrounding the Implementation of the United Nation’s Sustainable Development Goals on Micro and Macro Levels: \nUtrecht as a Global Goals City
Bibliographic record
Abstract
As the Sustainable Developments Goals (SDGs) are still a recently developed framework, not many studies have been conducted investigating the interplay between their implementation and discourse on macro and micro levels. More specifically, no previous studies have focused on investigating the specific local implementation of a Global Goals city. Therefore, this thesis aimed to provide an overview of the local approach of Utrecht, a Dutch Global Goals city, using theories of intercultural and organisational communication. These include theories such as the concept of discourse, the Montreal School’s theory of text and conversation, the Four Flows and Holliday’s (2016) concepts of threading and blocking. The analysis was performed using methods such as a document analysis, semi-structured interviews and ethnographic research. The subject of the document analysis was a webpage from the municipality’s official website and the interviewees included a representative of Utrecht’s municipality and of the organisation Utrecht4GlobalGoals. The ethnographic research was focused on three projects in Utrecht. The results showed that Utrecht as a Global Goals city employs a bottom-up approach and makes use of storytelling on both local and international scales to increase awareness of the SDGs. Additionally, it was found that the discussed theories served as good models to describe the discourse surrounding the implementation of the SDGs, as well as to analyse Utrecht’s local organisational culture as a Global Goals city. Finally, the SDGs were found to be used as a common language, as well as a way of threading between different cities and countries.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".