Mapping sustainability assessment and reporting in the UK tertiary education : A whole-institution perspective on sustainability assessment and reporting tools
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».