Charting the path after 2030: what should higher education’s role be in the future of the sustainable development agenda?
Bibliographic record
Abstract
As 2030 approaches, it is clear the Sustainable Development Goals (SDGs) will fall short of their ambitious targets, raising questions about the future of the global development agenda. The current moment presents an opportunity for reconsidering the role of higher education in charting that future. In this Forum, we contribute to growing debates on the SDGs’ future and examine how universities might shape what comes next. We invited scholars to respond to a common prompt: what should follow the SDGs after 2030, and how can universities better contribute to or critique global development agendas? In three distinct pieces, contributors interrogate the economic hegemony of development thinking, advocate for post-carbon futures, and emphasise the value of Indigenous knowledges. Together, they call for reimagining universities as sites of critical inquiry, plural knowledge production, and social transformation in advancing just and sustainable futures.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".