Integrating education for sustainable development into a higher education institution: beginning the journey
Bibliographic record
Abstract
Much of the current literature on integrating sustainability into HEIs is focussed on why HEIs should embrace sustainable development (SD) and what is still missing or hindering work and the integration of efforts. There is much less exploration of how SD has been interpreted at the individual HEI level and action taken as a result. This case study reflects on important elements of the journey Nottingham Trent University (NTU) in the UK has taken to integrate sustainability, focussing on key decisions and activity in 2009/10. In highlighting this, the authors seek to empower those looking to support and/or lead the embedding of Education for Sustainable Development (ESD), separately or as part of an integrated effort, in their own institution. Today in 2019, NTU is a global leader in integrating ESD as part of a wider SD agenda. The work which this paper presents, to understand and establish a baseline of key elements of NTU's existing ESD activity and systems, was an important turning point. Activities undertaken to review and assess “where are we now?”, primarily through an institution-wide survey in 2009/10, led to important insights and supported dialogue, as well as the connection and underpinning of core administrative elements of the NTU SD framework and systems. Further recommendations are given in the final section of this paper on other drivers that can help to embed ESD within an HEI.
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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 | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".