Notice bibliographique
Résumé
One needs only to read the headlines to know that the climate crisis is upon us. United Nations Secretary-General Antonio Guterres suggests that we have passed from a warming phase into an “era of global boiling” (Reuters 2023). Climate scientists argue that the climate tipping point is likely closer than we suspect, noting that the Atlantic Ocean's circulation system, the Atlantic Meridional Overturning Circulation, will likely experience an abrupt and irreversible change due to warming as early as the middle part of this century, which will have widespread consequences (Ditlevson and Ditlevson 2023). We are now consistently experiencing the hottest months in history, and under an almost constant threat of volatile weather. Climate refugees are no longer a possibility, but a reality. The climate crisis may be the greatest threat colleges and universities face in this already volatile, uncertain, chaotic, and ambiguous century. Global warming threatens to squeeze, injure, and destroy institutions and the many purposes we invest in them. Yet at the same time as academia faces this challenge, we also have the opportunity to improve humanity's ability to understand, mitigate, and adapt to the emergency (p. 225). This call to action is amplified by climate writers and activists such as Joanna Macy, Katharine Hayhoe, and Bill McKibben, and the responsibility and ability of colleges and universities to respond to social and ecological crises is evident in pedagogy studies and philosophy of education, such as Kevin Gannon's Radical Hope, Paul Handstedt's Creating Wicked Students, David Orr's Earth in Mind, Alexander's Universities on Fire, and bell hooks’ Teaching to Transgress. This special issue explores higher education's ability to respond to the climate crisis through sustainability pedagogies in teaching and learning, program design, and cross-institutional collaborations. The first chapter explores many levels of teaching and learning through the lens of the Climate Alliance's “Just Transition Framework.” Applied to higher education, this framework issues a call for educational developers to work with faculty on course design aimed at justice and sustainability, supporting practices that are restorative and regenerative, rather than extractive. In chapter two, a group of educational developers and faculty at Barnard College share their both challenging and successful experience in facilitating co-taught, interdisciplinary courses on climate. They explain how they surmounted institutional and disciplinary barriers and what kind of programming was effective in creating partnerships across disciplines. Chapter three details the history and structure of one of the country's first online sustainability and resilience graduate programs. Developed at Green Mountain College and sustained by Prescott College, this program is centered on place-based pedagogies, even though it is primarily asynchronous and remote. Chapter four chronicles a sustainability pedagogy cross-institutional dialogue between educational developers and faculty at several universities in Canada. This program has yielded expected results, such as participants learning from the challenges and successes of other institutions, but it has also led to other benefits, such as research collaborations and a feeling of community. Chapter five showcases the interaction between a statewide initiative in Alaska and a particular class in the arts, emphasizing how project-based learning can lead to justice and sustainability-focused outcomes. This, in addition to the preceding chapters, is aimed at offering examples for and guidance on various high impact practices that will enhance our universities’ capabilities to contribute positively to social and environmental sustainability and resilience. Finally, the issue concludes with an opinion piece on artificial intelligence. The author reminds us of the significant environmental footprint of AI tools and asks us to consider taking a pause in what seems to be a frantic race to keep up with and leverage this technology. These suggestions come not only from environmental concerns, but the author argues that our pedagogies may be stronger and more resilient if we are more thoughtful in our adoption of AI. This is not an argument for ignorance but rather a statement that the worth of education must now be measured against the standards of decency and human survival—the issues now looming so large before us in the twenty-first century. It is not education but education of a certain kind that will save us (p. 238).
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 | 0,001 | 0,008 |
| 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 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».