A blueprint for what? From a critical policy discursive analysis of UN’s sustainable development goals to a constructive rearticulation for their application
Bibliographic record
Abstract
Halfway through the UN 2030 Agenda of Sustainable Development Goals (2015–2030), the achievement of most of the proposed targets have been lagging behind, as has been confirmed in recent UN and UNESCO reports. While these reports mostly provide external features which cause the delay, this paper analyses and addresses possible features within the UN 2030 Agenda which might explain that shortfall. These include an unflagging belief in economic growth and a lack of an analysis of causes, as well as problems to do with costs and benefits of particular SDGs. Hence, the application of some SDGs might be counterproductive for the environment – and thus for sustainability. This article highlights outcomes of analyses of the Agenda, zooms in on SDG4 on education and presents alternative, more promising avenues concerning the SDGs. The 2030 Agenda and the alternative approaches are interpreted in terms of a shallow ecological (mechanistic) and a deep ecological (organic) worldview. We then propose ways forward for critical policy discourse analysis that may enhance the capacity of the UN 2030 Agenda in the direction of what they are meant to do: global cooperation toward a sustainable rearrangement of human life on earth.
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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 | 0.064 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.093 |
| Scholarly communication | 0.036 | 0.043 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 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".