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Hope and the grand challenges:the (new) priorities of the contemporary university

2022· article· en· W4412322997 sur OpenAlexaffabout
James Arvanitakis, Sharon Rider, David Hornsby, Søren Smedegaard Ernst Bengtsen, Ryan Gildersleve

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueDiverse Education Studies and Reforms
Établissements canadiensCarleton University
Organismes subventionnairesnon disponible
Mots-clésGrand ChallengesEnvironmental ethicsLibrary sciencePolitical scienceSociologyComputer sciencePhilosophyLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The year of 2020 has highlighted the brutal realities confronting our contemporary society: from the wildfires in Australia reinforcing the consequences of human-driven climate change, to the assassination of Iranian military commander Qasem Soleimani that has led to new-found tensions in the Middle East, to the outbreak of the coronavirus and the growing political upheaval as highlighted in the impeachment proceedings (and subsequent acquittal) of President Trump, the challenges facing liberal democracies have never been more pronounced. These grand challenges cut across political, social, economic and cultural spheres and are accompanied by an unprecedented loss of trust in expert institutions (Pew 2019). This has occurred as the neoliberal experiment has sidelined institutions such as universities that are driven by the public good. In fact, the impacts of neoliberalism have manifested themselves in many ways that would have been difficult to foresee: from the focus (some would say obsession) on international rankings, casualisation of faculty that now sees an increasing reliance on precarious labour, attacks on academic freedom, cultural wars and questions regarding student preparedness. Additionally, the rising costs of higher education have placed strain on graduates and have many prospective students questioning the value of undertaking study. This has been aggravated by the ongoing criticism by certain elements of the private sector that argue universities are not producing ‘work ready graduates’ as if the primary role of higher education is an apprenticeship for large, multinational organisations. This sense of universities being under siege is reflected across many other institutions of liberal democracies including the mainstream media and the public service. The media, for example, is increasingly seen in partisan terms. Different political actors accuse each other of producing fake news when they see reports they disagree with. If Benedict Anderson (1983) argued that the printing press led to national newspapers that created an ‘imagined community’ having national conversations, the splintering of the media landscape is directly resulting a growth of political ‘echo chambers’ and a breakdown in the connections we had. Within this context, many universities are reflecting on the very reason for existing. This includes work on the cultures they represent and are (re)producing. That is, do higher education institutions further cement power relations and create greater inequalities while, as Thomas Frank (2019) argues, echoing the myth of a meritocracy. If, as former IBM chairman Louis V. Gerstner, Jr. (2003) argued, ‘culture is everything’, then the exact culture of the contemporary university is one of uncertainty. It is within this context that universities and the scholars that occupy these institutions, have the opportunity to re-establish their authority. That is, move away from the neoliberal framework of competition, commodification, consumerism, and corporatization that has driven many of university priorities over the last three decades. This starts with re-establishing frameworks that our institutions were built on: cooperation, civic duty and stewardship as well as prioritising the pedagogy of the citizen-scholar (Arvanitakis and Hornsby 2016). It is from this perspective that universities have the potential to lead in confronting the grand challenges and promote a sense of hope that stimulates engagement and empowerment rather than cynicism and distrust. In other words, universities should use this moment of being ‘under siege’ and stake their claim for what they want to be, how they want to function and outline a new social contract between our institutions, the government, the public and other important stakeholders. In so doing, they can outline their vision, not as discreet units in competition for students and research funding, but as an interdependent sector whose focus is responding to the many grand challenges. This should include a re-imagination of what the contemporary curriculum is and how to engage with a student body who feel pressure to complete their degrees and ‘find work’ to pay off debt. While such goals sound idealistic and lofty, there are already many examples where such approaches have already been embraced. The ‘citizen scholar’ program, for example, emerged at Western Sydney University as a way of promoting both scholarship and citizenship. This pedagogical program, rather than being seen as proprietary knowledge, has been openly shared with other universities in Australia, India, South Africa and Canada. Likewise, universities are finding ‘shared research facilities’ much more efficient and effective than individually chasing funds. While there is no doubt that universities feel they are under siege, a commitment to a new approach means that this could be an important turning point for higher education. Anything less would place our institutions, and democracy, in a precarious position.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,013
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,984
Score d'incertitude au seuil0,081

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0130,013
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0160,021
Communication savante0,0240,026
Science ouverte0,0020,014
Intégrité de la recherche0,0120,022
Charge utile insuffisante (le modèle a refusé de juger)0,0240,005

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.

Tête enseignante Opus0,043
Tête enseignante GPT0,251
Écart entre enseignants0,208 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission2
Résumé présentoui

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