Mapping sustainability assessment and reporting in the UK tertiary education : A whole-institution perspective on sustainability assessment and reporting tools
Bibliographic record
Abstract
A plethora of assessment and reporting ‘tools’1 has become available for the improvement of sustainability performance in Further and Higher Education(FHE). As the profile of sustainability reporting and assessment continues to rise, the market of FHE sustainability assessment and reporting tools is expanding with more than a hundred ‘tools’ in use by UK FHE institutions, as identified at the initial phase of this research. Navigating through this increasingly complex landscape often seems a daunting task. In response, the EAUC has initiated this project to help sustainability professionals critically evaluate those tools and facilitate their institutions’ orientation in sustainability assessment and reporting. The project has achieved three main results:\n- Firstly, tools of importance to the UK FHE sector have been identified and are presented in the form of a guide providing an overview of each (pp. 11-30). - Secondly, these diverse tools have been ‘mapped’ under a whole institution approach framework, as modelled by LiFE (a self-assessment and reporting mechanism developed by the EAUC). They are colour coded according to their level of alignment with the LiFE criteria. This ‘mapping’ allows identification of emphases or gaps in the FHE sustainability coverage for each tool. Institutions are thus provided with a\nwhole institution visual analysis of the scope and impact of tools they might have in place or are considering adopting. (Appendices A and B display these maps). - Thirdly, a ‘Dashboard’ has been developed to compile all the tools and systematize their comparison and analysis. The dashboard tools also include an allocated score on the basis of its coverage of the whole institution sustainability, as defined by LiFE. (Section Dashboard Methodology: pp. 9-10). The Dashboard provides a mechanism for creating customised ‘baseline’ maps which will include all an institution’s tools to identify gaps and further drive performance (Appendix A) Our study features alternative tools that go beyond eco-efficiency, addressing areas of FHE sustainability\nsuch as teaching and research. Having said that, it is crucial to stress that this research does not endorse any of the tools, which were chosen on the basis of their frequency of use and importance to the EAUC members. In other sectors, there is a tendency for tools to harmonize with each other, creating a common language of indicators which enables institutions to better communicate and compare their sustainability performance. We have tried to do the same for the FHE sector. The EAUC will continue to work closely with institutions through an approach which recognises the importance of both external tools and internal programmes to performance improvement, assessment and reporting. This research also communicates the merits of combining both internal and external approaches within a whole institution framework. In the\nspirit of the EAUC’s approach, we take this opportunity to highlight that the success of this project is impingent upon the support and participation of our member institutions. Thus, we invite feedback, ideas and contributions which will help to shape this co-creation between the EAUC and its members. To get involved or for further information, please contact Iain Patton at info@eauc.org.uk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".